System and method for overcoming decision making and communications errors to produce expedited and accurate group choices

ABSTRACT

A system and method is provided overcoming decision-making and communications errors to produce expedited and accurate group choices. The invention provides collective outcomes that are resilient to communication and decision making errors, and which are provided with a minimum wait time. The system comprises a user interface engine that provides a channel to the features of the present invention, an agenda manager module for creating and presenting questions, a user manager module that controls interactions with user who request questionnaires, submit response data, and request access to analytical results, and a report manager module that identifies collective outcomes that are resilient to error and/or that weight individual votes to optimize the group&#39;s performance in producing one or more correct or optimal collective choices. A common data exchange allows communication between the modules.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.10/953,514 filed Sep. 29. 2004, now U.S. Pat. No. 7,172,118, whichclaims the benefit of U.S. Provisional Application Ser. No. 60/506,825filed Sep. 29, 2003, the entire disclosures of which are both expresslyincorporated herein by reference.

STATEMENT OF GOVERNMENT RIGHTS

The present invention was supported in part by the U.S. Army,TACOM-ARDEC Contract No. DAAE30-00-D-1011. The Government may havecertain rights to this invention.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to the processing of individual choices toproduce expedited and accurate collective outcomes. More specifically,the present invention relates to a system and method for overcomingdecision-making and communications errors to produce expedited andaccurate group choices.

2. Related Art

In the past, wired and wireless networks have been used to processindividual choices or votes into collective outcomes in decision rooms,surveys, polls, and other collective decisions. However the softwaresystems used in such applications are not designed to withstandcommunication errors, do not enable a group to reach a consensus eventhough all of the votes have not been received for processing, and donot weight individual votes on one or more dimensions of choice tomaximize the group probability of making one or more correct or optimalcollective decisions. As such, these problems limit the reliability andaccuracy of group decisions.

Communication errors can be caused in numerous ways, including maliciousphysical or cyber attacks against the network and equipment failure(e.g., link or node failure in the network). In the case of wirednetworks, destroying or damaging the nodes or links in the network candelay and/or thwart the delivery of votes to be processed intocollective outcomes. Cyber attacks can use computer viruses or worms todestroy software systems and/or data required in the collection andprocesses of individual choices into collective outcomes. These attackscan include viruses that overload node capacity (creating DOS (Denial ofService) effects) or network links (creating network errors inconnecting to destination nodes), as well as intrusions that occur whenauthentication, encryption, server management tools, and other securitytechniques are penetrated.

Communication breakdowns can also be caused by so-called “benign” orinadvertent errors that occur because the programming tools used tocreate the software systems contain loopholes or faults that can lead tomalfunctions in the submission and processing of votes. These types oferrors will not only produce counting errors that are likely to goundetected and uncorrected, but also provide an opportunity formalicious actions to take place (see, e.g., F. B. Schneider, G.Morrisset, and R. Harper, “A Language-Based Approach To Security”, in R.Wilhelm ed., Informatics: 10 Years Back, 10 Years Ahead, Lecture Notesin Computer Science, Volume 2000 Springer-Verlag, Heidelberg, 2000). Forexample, when a buffer overflow occurs in submitting data to a database,the error provides an opening that can be exploited by an individual orsoftware process to gain access to the database to change, destroy,and/or damage data.

Benign errors can also be caused by noise created when transmitter powerlevels and the number of terminals are not optimized for sending data toa wireless base station. (see, e.g., D. Goodman, Zory Marantz, PeninaOrenstein, Virgilio Rodriguez “Maximizing the Throughput of CDMA DataCommunications,”http://utopia.poly.edu/˜vrodri01/papers/vtc_gmpr03.pdf.)

Although malicious and benign errors can occur in wired and wirelessnetworks, both types of errors have a greater impact in wirelessnetworks than they do in wired networks. In wired networks, for example,adaptive routing can enable the votes to be submitted successfullydespite physical attacks on particular nodes or links in the network.Similarly, the greater processing power and energy capacity of wirednodes enables the use of intrusion detection and correction software tocounter cyber attacks. In contrast, in wireless networks, connectivityis not is as flexible or responsive and mobile devices lack theprocessing and energy capacity to adapt to the challenges posed bymalicious and benign communications errors.

In these fragile communications environments, both types of errors canhave deleterious effects on the ability of groups to reach a consensusand/or to produce an accurate collective choice. These effects can makeit impossible for a group to agree and take action and/or produce acollective outcome that provides one or more correct or optimal choicesto be carried out. In the first case, for example, malicious and/orbenign communications errors may make it impossible to collect enoughvotes to determine if there is a majority consensus. Even if theaggregation rule is plurality, not majority, missing data may make itimpossible to determine if the current plurality winner would be theeventual winner if it were possible to collect and count all the votes.In such cases, the group would not be able to take action to protectitself or to participate as part of a broader collective action toachieve particular objectives. The resulting loss of money, property,and life can be tremendous. In the second case, when the group ischarged with reaching a consensus to find correct or optimal answers toone or more decision tasks, benign and malicious communications errorscan have a filtering effect that prevents the most competent voters fromsubmitting their votes, thereby allowing the collective decision to bedominated by the least competent voters. Collective incompetence alsoentails significant losses.

Even when malicious and benign communications errors do not presentobstacles to processing individual choices into collective outcomes,time constraints can make slowly-produced collective outcomesirrelevant. For example, if a group of investors cannot reach aconsensus before a deadline passes, they will miss an opportunity.Similarly, if a network of military decision makers cannot expeditiouslyreach a consensus about the capabilities of an approaching adversarialforce, they may lose many lives—including their own. In both of theseexamples, the decision tasks may involve the selection of one or morecorrect or optimal choices. For instance, if the investors reach aconsensus in time, but their collective outcome is wrong, they may notmaximize the benefits derived from the opportunity. In fact, theinvestors may experience a disastrous loss instead of even a modestgain. Similarly, if the military decision makers produce collectiveoutcomes that are very accurate, they may save many lives. However, asthe accuracy of their collective decisions declines, the number ofcasualties will rise.

Another limitation of the state of the art is the exclusive focus onproducing winning coalitions or decisive collective outcomes. Whethercollective outcomes are constrained by time or malicious and/or benigncommunications errors, knowing if there is going to be a tie or anindecisive collective outcome can enable decision makers to takeimmediate action to collect additional information and/or followcontingency measures to minimize losses. For example, a tie produced bythe default one person, one vote and plurality aggregation rule methodmay be resolved by applying an alternative method. If the voters ratedall of the choices on an ordinal preference scale before voting fortheir most preferred choice, the rating data can be reprocessed underCondorcet scoring (based on binary contests among all of the choicesacross voter preference orderings), and the tie can be assessed todetermine if one (or more) of the tied outcomes is a Condorcet winner.Knowing if there will be no collective consensus also provides anopportunity to launch a followup query to see if the group can reach aconsensus on a different decision task.

State-of-the-art systems and methodologies for collecting informationabout individual preferences and judgments do not include votingmechanisms for dealing with communications and/or decision-makingerrors, nor can they adequately deal with emergencies or urgent timeconstraints. For example, polling and survey software does not includesuch mechanisms, nor do decision rooms with Group Decision SupportSystems (GDSS) tools. GDSS tools rely on human facilitators, who cannotprocess information quickly and accurately enough—even with a GDSS—toaddress error and time constraints. Even when moderate time constraintsallow a human to facilitate the production of a collective outcome, GDSSsystems and methodology is limited by one-dimensional data collectionand analysis and limitations on file functionality. Moreover, GDSS'srequire users who are relatively computer-savvy and comfortable usingdifferent computer tools. Further, GDSS quality is limited bydifficulties in recruiting and retaining skilled facilitators (see,e.g., R. Chapman, “Helping The Group to Think Straight,” DarwinMagazine, August, 2003).

Current GDSS, polling, and survey solutions are also limited becausethey are not designed to process voice and/or gesture information asvoting inputs or outputs. For example, although analog and digital voicetechnology is used to authenticate participants in a collectivedecision, they are not used to communicate information in novel ways totake advantage of the efficiency and effectiveness of representingpreferences and/or judgments in digital and/or analog form. For example,voting by representing preferences by a preference range can providericher input information than simply selecting a single point along ascale. Moreover, when individual analog inputs are processed to producea collective outcome, the results can provide a more accurate andeasily-computed view of the voting results.

Voice voting is a very “noisy” means of measuring preferences. For thisreason, voting protocols such as Roberts Rules of Order only describeits use in binary decisions in which the “yays” can be readilydistinguished from the “nays.” Still, voting theorists such as Condorcetrecommended avoiding voice votes and Roberts Rules of Order prescribesthe use of other voting mechanisms (show of hands, division of thewhole, and/or ballot) to scrutinize the voice outcomes. Digitalexpression of voice votes can be used to improve the efficiency andaccuracy of voice voting. Although voice votes could still beinterpreted in analog mode, digitized voice inputs would integrateauthentication (via techniques such as voice prints) with representationof intensity of preference based on pre-existing profiles that revealpersonal ranges of intensity for each individual. Processing such inputswould make it unnecessary to clarify the outcomes of voice votes byusing division of the whole, show of hands, or ballots.

Another limitation of the state of the art is the circumscribed use ofmobile sensors in collective decision making. Currently, sensors reportreadings for environmental agents to a host machine where the data areaggregated to generate a report. Methodological and system constraintslimit the precision and accuracy of the reports because simpledistributional statistics must be used to describe phenomena. Sensorsare not used to submit ratings based as if they were human decisionmakers expressing a preference over a list of choices or rendering abinary or rendering a binary or more complex judgment based rules ofartificial intelligence for generating these preferences and/orjudgments. Communications errors and malfunctions of sensors are tworeasons that sensor collective decisions have not been developed.

The distribution and management of electricity in national network gridsis a serious problem that produces blackouts that cause significanteconomic harm and dislocation. Although recent problems seem to havebeen caused by “benign” errors associated with overloading nodes andlinks, solutions to these problems have focused on attenuating errorsand restabilizing the transmission of electricity once networks havebroken down. What is needed is a flexible methodology and system toprevent network breakdowns from occurring. This solution would allow thenetwork to sustain the flow of throughput and minimize vulnerability todestabilizing events. This type of solution is important for dealingwith terrorists who could initiate cascading “benign” errors into amalicious cyber attack on the United States.

Accordingly, what would be desirable, but has heretofore not beenprovided, is a system and method for overcoming decision-making andcommunications errors to produce expedited and accurate group choices,which overcomes the aforementioned shortcomings.

SUMMARY OF THE INVENTION

The present invention provides a system and method for overcomingdecision-making and communications errors to produce expedited andaccurate group choices. The system of the present invention includes aplurality of computing systems interconnected by a communicationsnetwork, each of the plurality of computing systems including a userinterface for allowing communication with a voter at each computingsystem; an agenda manager module for creating and presenting at leastone question to be voted on to each voter using the user interface;means for calculating a voting termination point based upon vote scoringmethods and a voting objective; a user manager module for controllinginteractions between each voter and receiving votes up to the votingtermination point; and a report manager module for processing the votesby applying a plurality of vote scoring methods to produce a collectivegroup decision that is resilient to errors. A common data exchange isprovided for allowing communication between each of the modules. Theinvention can be implemented on a plurality of computing devicesconnected by a network, wherein voters can vote using the computingdevices. The devices could be wirelessly connected, and could be mobile.

The present invention further provides a method for producing anerror-resilient collective group decision from a plurality of voters ona communications network. The method comprises the steps of:establishing a voting agenda having at least one of question to be votedon; determining a voting objective; presenting the voting agenda to eachof the plurality of voters; calculating a voting termination point basedupon vote scoring methods and the voting objective; allowing each of theplurality of users to vote; receiving votes until the voting terminationpoint; and processing the votes with a plurality of vote scoring methodsto produce a collective group decision that is resilient to errors.

The present invention also provides a method for deploying resources.The method comprises the steps of: providing a communications networkinterconnecting a plurality of voters with a command center; issuing avoting agenda from the command center to each of the plurality ofvoters; calculating a voting termination point based upon vote scoringmethods and a voting objective; allowing the voters to vote; terminatingvoting at the voting termination point; processing the votes using aplurality of vote scoring methods to produce a collective groupdecision; and deploying resources based upon the collective groupdecision.

The present invention further provides a method acquiring data from aplurality of sensors. The method comprises the steps of: providing acommunications network interconnecting a plurality of sensors with acenter processor; determining a plurality of vote scoring methods;calculating a data acquisition termination point based upon vote scoringmethods and a voting objective; acquiring data from the plurality ofsensors until the data acquisition termination point; and processingacquired data using the plurality of scoring methods to produce acollective group decision.

BRIEF DESCRIPTION OF THE DRAWINGS

Other important objects and features of the invention will be apparentfrom the following Detailed Description of the Invention taken inconnection with the accompanying drawings, in which:

FIG. 1 is a diagram showing the components and overall architecture ofthe system of the present invention for group decision making.

FIG. 2 is a diagram showing the user interface engine of the presentinvention in greater detail.

FIG. 3A is a diagrams showing the agenda manager of FIG. 1 in greaterdetail.

FIG. 3B is a diagram showing the question creation module of FIG. 3A ingreater detail.

FIG. 3C is a diagram showing the agenda templates of FIG. 3A in greaterdetail.

FIG. 3D is a diagram showing the sample voting rules of FIG. 3A ingreater detail.

FIG. 4A is a diagram showing the user manager of FIG. 1 in greaterdetail.

FIG. 4B is a flowchart showing processing logic of the vote datacollection module of FIG. 4A.

FIG. 4C is a flowchart showing processing logic of the voteridentification module of FIG. 4A.

FIG. 4D is a diagram showing the trust profile module of FIG. 4A ingreater detail.

FIG. 5A is a diagram showing the report manager of FIG. 1 in greaterdetail.

FIG. 5B is a flowchart showing processing logic of the scoring moduleshown of FIG. 5A.

FIG. 5C is a flowchart showing processing logic of the error-resilientprocessing module of FIG. 5B.

FIG. 5D is a flowchart showing processing logic of the optimization voteprocessing module of FIG. 5B.

FIG. 5E is a flowchart showing processing logic for performing botherror-resilient and optimization vote processing.

FIG. 5F is a flowchart showing processing logic of the plurality voteprocessing module of FIG. 5B.

FIG. 5G. is a flowchart showing processing logic of the approval voteprocessing module of FIG. 5B.

FIG. 5H is a flowchart showing processing logic of the risk analysismodule of FIG. 5C.

FIG. 5I is a flowchart showing processing logic of the error-resilientoutcome analysis module of FIG. 5H.

FIG. 5J is a flowchart showing processing logic of the stochastic riskanalysis module of FIG. 5H.

FIG. 5K is a flowchart showing processing logic of the trust riskanalysis module of FIG. 5H.

FIG. 6 is a diagram showing sample data formats utilized by the presentinvention.

FIG. 7 is a diagram showing sample common data exchange formats utilizedby the present invention.

FIG. 8 is a diagram showing sample attachment file formats utilized bythe present invention.

FIG. 9 is a diagram showing the architecture and sample record format ofthe agenda database of the present invention.

FIG. 10 is a diagram showing sample authentication, encryption, andtrust parameters utilized by the present invention.

FIG. 11 is a diagram showing a sample centralized voting architecture inwhich the present invention can be implemented.

FIG. 12 is a diagram showing a sample decentralized voting architecturein which the present invention can be implemented.

FIG. 13 is a graph showing the relationship between individual and groupcompetence in the Condorcet “jury” theorem.

FIGS. 14A-14C are graphs showing test results of error-resilientcollective outcomes produced by the present invention using random tiebreaking.

FIGS. 15A-15C are graphs showing test results of error-resilientcollective outcomes produced by the present invention using no tiebreaking.

FIG. 16 is a graph showing variance and standard deviation outcomes forerror-resilient collective outcomes produced by the present invention.

FIGS. 17A-17C are graphs showing benchmark predictions of three scoringmethods using no tie breaking and homogeneous preferences.

FIGS. 18A-18B are graphs comparing the error-resilient collectiveoutcomes of the present invention and benchmark predictions.

FIGS. 19A-19B are graphs comparing the error-resilient collectiveoutcomes of the present invention and benchmark predictions.

FIG. 20 is a graph showing the effects of false positives on threevoting methods.

FIG. 21 is a graph showing the effects of false positives on threevoting methods.

FIG. 22 is a graph showing the effects of false positives on threevoting methods.

FIGS. 23A-23C are graphs comparing the probability of producingerror-resilient collective outcomes and benchmark predictions.

FIGS. 24A-24C are graphs showing the efficiency and effects of time onthe probability of producing error-resilient outcomes.

FIGS. 25A-25C are graphs showing the efficiency and effects of time onthe probability of producing error-resilient outcomes.

FIGS. 26A-26B are graphs showing comparisons of the probability ofproducing error-resilient outcomes for three scoring methods based onhomogeneous and heterogeneous preferences.

FIGS. 27A-27B are graphs showing comparisons of the probabilities ofproducing error-resilient outcomes for three scoring methods and theeffects of time on homogeneous and heterogenous preferences.

FIGS. 28A-28B are graphs showing comparisons of the probabilities ofproducing error-resilient outcomes for three scoring methods in varioussensor networks.

FIGS. 29A-29B are graphs showing comparisons of three scoring methodsand effects of time on the production of error-resilient outcomes invarious sensor networks.

DETAILED DESCRIPTION OF THE INVENTION

The present invention relates to a system and method overcomingdecision-making and communications errors to produce expedited andaccurate group choices. The invention provides collective outcomes thatare resilient to communication and decision making errors, and which areprovided with a minimum wait time (referred to herein as“error-resilient” and “waitless,” respectively). The system comprises auser interface engine that provides a channel to the features of thepresent invention, an agenda manager module for creating and presentingquestions, a user manager module that controls interactions with userwho request questionnaires, submit response data, and request access toanalytical results, and a report manager module that identifiescollective outcomes that are resilient to error and/or that weightindividual votes to optimize the group's performance in producing one ormore correct or optimal collective choices.

A common data exchange using one or more data formats allowscommunication between each of the modules. Error resilience andoptimization can be achieved separately or together, depending on thedecision task. The invention can operate in client-server and/orpeer-to-peer mode, and enables analysts to save time, money, property,and lives by inferring a collective outcome despite missing voting dataand/or by weighting incoming votes to optimize collective performancedespite missing voting data. The present invention includes synchronousand asynchronous modes of interaction, communication, and analysis ofcollective choice results.

The present invention extracts and uses information from the applicationof mathematical algorithms for scoring voting data derived fromdifferent voting methods and statistical and analytical algorithms thatdescribe the conditions under which the scoring algorithms produceerror-resilient, waitless, optimized, decisive, and/or tied collectiveoutcomes. The extracted information is used to produce new knowledgeabout collective choice processes that can provide insight toindividuals in computer-mediated groups to design and interpretcollective decisions that enable them to produce expedited and/oroptimized collective outcomes.

The present invention allows users to gain insight into means forovercoming communications and individual decision making errors toidentify error-resilient and/or optimal collective choices ininterpreting collective outcomes. These insights may be produced incompletely automated mode in which the user interface engine modulefinds error-resilient and/or optimal collective outcomes and takesimmediate followup action to carry out and/or implement the collectiveresults and/or to conduct followup queries to learn more from some orall of the respondents on an antecedent collective decision. Inaddition, these insights may be presented to deliberating human or nodeusers to browse and select options for followup consistent with theirgoals and decision procedures.

The following intuitive example illustrates the logic of the invention.Suppose 10 voters (humans or computer-mediated devices such as sensors)are choosing between A and B by sending in a single vote for one ofthese two choices. If the outcome of the collective decision is decidedby majority rule and six votes have already been received, the currentcollective outcome (A) is final and decisive. A is a waitless anderror-resilient collective outcome because the outstanding votes cannotpossibly change A's victory—even though they may change the score. Thepresent and unique invention enables decision makers to gain suchinsights when the set of choices contains more than two choices, whenthese choices are rated on multiple dimensions, and when the number ofvoters is fixed or variable.

Glossary of Terms

The following descriptions are presented to clarify the features of thepresent invention for overcoming decision making and communicationserrors to produce group choices.

-   -   A voting system includes the following components:        -   Vote Endowment: the number of votes individuals have to            express their preferences.        -   Vote Allocation: constraints placed on the distribution of            the vote endowment. Typically, these constraints include no            saving or trading of votes.        -   Vote Aggregation: criteria such as plurality, majority, or            unanimity for pooling votes to produce a collective outcome.

Each voting system component can be weighted to regulate the productionof collective outcomes. Vote endowments can be weighted by role orperceived or measured performance. For example, a leader may be givenenough votes to veto (or stop) or dominate (or dictate) the productionof a collective outcome. Or individuals with high or low competence(measured by or attributed to their performance) can have their votesweighted accordingly. Vote allocations can be weighted by re-scoringratings or rankings under an alternative scoring method. For instance,if ordinal preference data processed under Condorcet scoring (whichcounts the number of times that each choice defeats every other choiceacross voter preference orders), the winner is the choice with the mostvictories. If the same ordinal data are re-scored under Copeland scoring(which subtracts the Condorcet scores in each binary choice relationshipto find a net measure of popularity), the winner is the choice with thehighest net score. In both cases, the weight or affect of individualvotes on the production of a collective outcome can be changed. Voteaggregation can also be weighted by analyzing the affects of uncollectedvotes on in existing collective outcome.

For example, if the current vote count shows that one choice has amajority of collected votes, analysis can determine if the majority canbe reversed by uncollected votes. If the existing majority cannot bereversed by any configuration of outstanding votes, it can beinterpreted as the outcome can be considered as final even though allvotes have not been received or processed. In some cases, theanticipated final outcome may be an indecisive or tied result. Outcomesthat predict victories, ties, or indecisive results are “waitless.” Ifdelays in receiving votes are caused by benign or maliciouscommunications error, waitless outcomes also produce an error-resilienteffect by allowing the group to overcome communications error to reach aconsensus.

Turning now to the drawings, FIG. 1 is a diagram showing the componentsand overall architecture of the system of the present invention,indicated generally at 10. The system 10 comprises a user interfaceengine 20, an agenda manager module 30, a user manager module 40, areport manager module 50, and a common data exchange 60. Each of themodules 30, 40, and 50 can communicate with each other using the commondata exchange 60 and one or more data formats 70. An agenda database 80is provided for storing voting agendas. The user interface engine 20,agenda manager module 30, and report manager 50 can communicate with anetwork 15, which could comprise an intranet, local area network (LAN),wide area network (WAN), the Internet, or any other suitablecommunications network. Further, the network 15 could be a wired orwireless network. The system 10 could be implemented indatabase-neutral, network-neutral, and/or platform-neutral environments.

The system 10 generates and communicates insights into group decisionsand provides guidance to users at each stage of the process of groupdecision making. The system 10 could be implemented in Microsoft Windows(a trademark of Microsoft), Linux, Unix, Java (a trademark of SunMicrosystems), and PHP with databases such as Access, SQL Server,Oracle, MySQL, and Postgres. The network 15 allows communication betweentwo or more nodes, which could comprise hosts or servers connected to acomputer network. Each node could comprise a personal digital assistant(PDA), personal computer (PC), thin client, workstation, server, or anyother desired computing system.

Operation of the present invention involves the use of the userinterface engine 20 on a node to send commands that create an input dataobject in the agenda manager 30. The agenda manager 30 also receivescommands from the user interface engine 20. The agenda manager 20communicates with the agenda database 80 to create the input dataobject. In a preferred embodiment of the present invention, the inputdata object comprises one or a number of questions relevant to acollective decision to be made by a group. Such sets of questions arecommonly known in the art as an agenda. Additionally, the input dataobject also includes settings that determine which users have access tothe agenda, and which are communicated to the user manager module 40.The user manager module 40 regulates access of each of the nodes to theinput data object.

The input data object is transmitted over network 15 to one or morenodes, which could be arranged in a centralized or a decentralized mode.The input object appears on a user interface generated at each node bythe user interface engine 20, for each node on the network 15 that hasaccess to the agenda. Users of each node then answer the questions posedby the agenda by filling in fields. Tile answers may change the order ofthe questions or the types of questions presented. Such contingenciesare programmed in the input data object. The completed input dataobjects are then communicated back to a server or host in centralizedmode (FIG. 11) or decentralized mode (FIG. 12), and are stored in agendadatabase 80. From time to time, the report manager module 40 accessesthe database 80 and creates an output data object that incorporates thestored answers to the questions. The report manager module 50 utilizesreport templates that process data as it is stored in the database 80. Areport template for error-resilience determines if the current outcomeis error resilient. If the collective outcome is not error resilient,the report manager module 50 can notify users who have a notificationprivilege that an error-resilient result has not been found.Additionally, the report manager module 50 can report the likelihood offinding an error-resilient collective outcome. If an error-resilientoutcome is identified, the report manager module 50 reports the resulton the interfaces of nodes of users who have been granted access to thereport. If the decision objective of an agenda is to identify one ormore correct or optimal choices, the report manager module 50 uses areport template to weight individual votes to optimize collectiveperformance.

Each of the modules of the system 10 processes input data andcommunicates the results among users of the system 10 to facilitateinsight and enable the users to implement timely and accurate collectivedecisions. Communication from one object to another is handled viacommunications settings in the user interface engine module 20. Thesystem 10 provides an environment that is transparent to a user,provides synchronous and/or asynchronous analysis of incoming votes toidentify error-resilient collective outcomes and/or to weight individualvotes to optimize the probability of making one or more correct oroptimal collective choices.

The user interface engine 20 issues commands to the agenda managermodule 30, the user manager module 40, and the report manager module 50to orchestrate the production of data collection and reporting oferror-resilient and optimal collective outcomes. For example, the userinterface engine 20 includes options for using multimedia, mechanical,touch-screen, and optical devices such as mice, pens, and keyboards aswell as voice and neurological mechanisms to enter data into the modulesand receive output. The user interface engine 20 uses different mediasuitable to the task at hand and provides redundant communication whennecessary.

FIG. 2 is a diagram showing the user interface engine 22 of the presentinvention in greater detail. The engine comprises a user interface 22that could be programmed in any suitable programming language, and aplurality of users 24. The users 24 could comprise an administrator 26,a respondent 28, and an analyst 29. Further, the users 24 could be humanbeings, a process or node acting as if it were a human being, or aphysical object programmed to act like a human being. The administrator26 has exclusive privilege to create and manage agendas, users, andreports. In creating agendas, the administrator 26 can define a list ofusers who can answer questionnaires and/or view reports.

Users who are given the right to answer questionnaires and/or viewreports can take on an administrative role in creating their ownagendas, users, and reports. By default, these derivative privileges donot apply to the agendas, users, and/or reports of the original agendacreator or other users. Further, the user interface engine 20 sets thepermissions that govern access to definitions of gradations ofindividual identity for users from complete anonymity to completeidentification as well as agendas and reports. All of these settings arepassed on to the user manager module 40 of FIG. 1 to implement inhandling transactions and interactions with users.

FIG. 3A is a diagram showing the agenda manager module 30 of FIG. 1 ingreater detail. The agenda manager module 30 comprises a questioncreation module 32, a question library 34, templates 36, and votingrules 38, each of which can communicate via the common data exchange 60.The question library 34, templates 36, and voting rules 38 provide arepository for storing individual questions and questionnaire templates,as well as analytical rules for applying techniques that use multiplescoring methods to identify collective outcomes that are resilient tocommunications error, and/or to weight individual votes to optimize theprobability that a group renders one or more correct or optimalcollective choices. The agenda manager module 30 also comprises accesslists and protocols that regulate the right of users to answerquestionnaires and see collective outcome results.

The agenda manager module 30 handles editing and presentation ofquestions and options for user selection of choices. The editingfacility creates an agenda and a list of agenda items to be voted on.The agenda can be created by brainstorming to create a list and thenevaluate it to identify items that should constitute the agenda.Further, the agenda can be created by selecting a pre-existing templateor model agenda for a task. Agendas created from scratch can be saved astemplates and agendas set up from a template 36 can be either edited ormodified to fit a situation. Agendas can be initiated locally orremotely by users who have permission to invoke the process ofcollecting and analyzing voting information. Agendas can also be createdin an interactive, dynamic way depending on the responses of individualsand/or the results of collective choice analysis of an error-resilientand/or optimal collective outcome.

The agenda manager module 30 provides a mechanism for collecting anddistributing voting information including animation, video (real-time orstored), graphics, sound, hologram, or any other digital or analog formfor representing information. Further, the agenda manager module 30 setsthe conditions for communications security which include protocols andtechniques for user authentication, secure transmission of vote andreport information, and database security. The user manager module 40 ofFIG. 1 enforces these settings.

The agenda manager module 30 provides a means for an individual user ora group of users to initiate a voting process by inputting data thatcreate the initial conditions that govern the production of a collectivechoice. These conditions include identification of an agenda (includingan agenda name, list of agenda questions, agenda backgrounddescriptions, multimedia attachments, beginning and ending time of thedecisions, voter identification, participant privileges, and votingobjective (error resilience or optimization). Voter identification andparticipant privileges are entered into the agenda database 80 of FIG. 1and managed by the user manager module 40 and the report manager module50 of FIG. 1.

A feature of the agenda manager 30 is the creation and scheduling ofsets of questions for distribution to users to gain their responses. Thequestions are stored in the question library 34. This feature is usefulbecause the question library 34 allows users to use existing templateswith or without modifications, create new questions, and/or use selectedquestions from the library. Scheduling allows a user serving asadministrator to set the beginning and end times for an agenda as wellas the list of participants and their privileges. These schedulingattributes of an agenda are entered into the agenda database 80 of FIG.1 and regulated by the user manager module 40 and report manager module50 of FIG. 1. These features can be invoked using commands entered usingcommon data formats over the common data exchange 60.

The question creation module 32 provides for the linking of questionsand question sets to reports generated by the report manager module 50of FIG. 1, to provide information about response rates and attitudesover time. This information is valuable because it enables the user tomake intelligent use of historical information stored in the agendadatabase 80 of FIG. 1. The templates 36 of the agenda manager module 30allow for the setting up of error resilient and optimized votingprocesses. The templates 36 enable a user to configure the system tomake use of methods of voting appropriate for an error-resilient and/oroptimization decision task. The voting rules 38 allow for theapplication of different voting rules or scoring algorithms to answerdifferent questions about the same data set.

The agenda manager module 30 of the present invention provides a meansfor an individual initiator or a group of initiators to input data thatcreate the initial conditions which govern a collective choice process.These conditions include identification of an agenda (including anagenda name, list of agenda items, agenda and agenda-item backgrounddescriptions, and multimedia information attached to the agenda andagenda items), timing of the decisions (when they begin and end andwhether they are synchronous or asynchronous), and determination ofparticipants. Attributes of the agenda such as the degree of voterprivacy and authentication standards are set in the agenda module, butenforced in the user manager module.

The agenda manager module 30 also provides a menu-driven system forsetting tip an agenda topic, adding agenda items, and attachingmultimedia files to agenda items. This feature is useful because itallows anyone to save time in making use of background informationrelated to a time-critical decision. This functionality makes itpossible to use different combinations of media for input and output tomatch the data entry requirements of the module with the needs andphysical capabilities of users. The integration of multimediainformation facilitates the use of industry standard files for graphics,images, animation, and video that can be located any place on a network.Further, this feature is significant because it provides a level playingfield of information used to render decisions. The significance of thisfeature is not only that decision makers can see and hear the sameinformation in a timely manner, but that this information serves as abasis for improving the efficiency of collective action as well as theeffectiveness of deliberation and debate.

A related feature of privilege setting in the agenda manager module 30of the present invention is the option of previewing and reviewingcollective choice results. The previewing privilege determines if aparticipant can gain access to the review module before or after allparticipants have cast their votes. Preventing access to collectivechoice data and analysis before all votes are collected and processed isuseful in prohibiting participants from monitoring incoming votes toobtain information that can be used to bribe, pressure, or persuadevoters. Restricting access to collective choice data even after allvotes are in can be used in private polls in which data are consideredto be confidential or sensitive.

Another related feature of privilege setting in the agenda managermodule 30 of the present invention is access to a notifications option.This option, contained in the report manager module 50, is selected inthe user interface engine 20 to control access to information about anongoing collective choice process that is derived from processing inputsinto the data collection module, where they are processed and output asinputs in the review module, where a decision analysis module within thepresent invention analyzes patterns and gleans insights for users thatare output within the review module. This feature is useful for settingup notification alerts related to the goals of producing error-resilientand/or correct or optimal collective outcomes. Users with this privilegecan receive updates how likely it is that a collective choice processwill produce an outcome that satisfies one or both of these decisionobjectives. These likelihoods may be derived from background Monte Carlosimulations that model different communications error conditions as wellas the probability that decision makers make correct or optimalconditions. A number of control variables can be used in suchsimulations, including, but not limited to: the number of voters; thenumber of choices; the number of dimensions on which the choices arerated; voter preference distribution (including rating scale); votercompetence (reliability) distribution; competence (reliability)weighting rules, including linear and non-linear rules; type of votingsystem; expression method (e.g., One Voter-One Vote (OVOV)); aggregationrule (e.g., plurality); tie-breaking rule (e.g., none, randomized,optimized); voter false positive rate; voter false negative rate; andvote arrival intervals.

If a collective decision is part of a dense sequence of group decisionson the same task(s), techniques such as factor analysis and stochasticdominance can be used to assess the risks of achieving error-resilienceor collective optimality. This feature is useful because it allowsdecision makers to adapt to changing conditions before enoughinformation has been collected to determine if a collective outcome willbe error resilient and/or optimal. This insight is particularly usefulwhen it is projected that a collective outcome will be a tie or anindecisive result. Knowing that these projected outcomes are likelyallows the user of the user interface engine module to take immediateaction to gain more information and/or to begin to implement contingencyplans.

FIG. 3B is a diagram showing the question creation module 32 of FIG. 3Ain greater detail. The question creation module 32 allows for a numberof options and parameters regarding question creation and scheduling tobe set. A number of library options 321 can be ascertained from theuser, and include: creating a new question, adding or modifying a ratingscheme, adding or modifying a selector, and selecting questions from anagenda. These options and parameters can be stored in the questionlibrary 34 of FIG. 3A. Additionally, the question creation module 32allows the user to define a number of scheduling options 322. Suchoptions include, but are not limited to, the identities of participantsin desired questions, the starting and ending times for voting,contingency parameters, and whether one or more agendas is activated(turned on) or deactivated (turned off).

FIG. 3C is a diagram showing the agenda templates 36 of FIG. 3A ingreater detail. As mentioned earlier, the templates 36 allow a user toconfigure the system to make use of methods of voting appropriate for anerror-resilient and/or optimization decision task. The templates 36 arealso useful when the user can only approximate the expected number ofparticipants with an order of magnitude number or a possible range ofparticipants. For example, if 100 participants are expected, but thenumber increases to 10,000 or decreases to 20, the templates 36 adapt tothese conditions and implement the most feasible and efficient method ofcomputing collective outcomes. In addition, this feature can usehistorical or simulation information to estimate the likelihood thaterror-resilient collective outcomes can be produced. This informationallows the user to make informed choices about implicit risks associatedwith setting lower and upper limits on the number of participants. Asshown in FIG. 3C, the templates 36 could comprise error-resilienttemplates 361 and optimization templates 362. The error-resilienttemplates 361 collect information relating to resource allocation,target selection, facial identification, route selection, saved templateapplications, and new templates. The optimization templates 362 collectinformation relating to resource allocation, target selection, targetidentification, facial identification, route selection, investmentchoices, key indicator assessments, saved templates, applications, andnew templates.

FIG. 3D is a diagram showing the sample voting rules 38 of the agendamanager module of FIG. 3A. The voting rules 38 could comprise rules 381tailored to endowment and allocation, and rules 384 tailored toaggregation. The endowment rules 382 could process votes in accordancewith one or more endowment rules, such as: n votes, where n is less thanor equal to the number of choices; one vote for one of n choices; onevote for each approved choice; or ranked choices. The allocation rules383 could process votes in accordance with one or more allocation rules,such as: savable votes; votes tradable for votes or money; one vote foreach approved choice; or m out of n choices. The preference rules 385could comprise plurality, majority, or unanimity aggregation rules. Thecompetence rules 386 could comprise a priori deterministic andstochastic criteria, self-ratings, and demographic qualifications. Morethan one voting rule can be implemented to allow for analyzing votingprocesses with more than one possible aggregation rule. For instance,under approval voting (which allows users to cast one vote for eachapproved choice), conventional aggregation rules such as plurality,majority, and unanimity have different properties. For example, underapproval scoring, ties can occur under plurality, majority, andunanimity rules. In addition, these rules can be defined on the basis ofthe total number of approval votes cast or the total number of voterswho cast an approval vote for a choice.

Any desired voting rule, or a combination thereof, could be implementedby the present invention, depending upon voting circumstances. Forexample, plurality scoring could be implemented to reveal which choicewas most frequently selected as the top choice in voter preferenceorderings. Condorcet scoring could be implemented to reveal which choicewas most frequently preferred to every other choice in binarycomparisons across voter preference orderings. Copeland scoring could beimplemented to show how much more each choice was preferred to everyother choice in binary comparisons across voter preference orderings.Approval scoring could be implemented to reveal which choice(s) was mostfrequently approved by voters. (Mackenzie, D. “May the Best Man Lose,”Discover Magazine, November, 2000.) Such voting rules can be used toresolve ties or scrutinize indecisive collective outcomes. In the caseof ties, for instance, if there is a tie under approval scoring,Condorcet or Copeland scoring algorithms can be applied to measure thestrength of support for the tied choices.

Another feature of the analysis of incoming votes under differentscoring methods is using more than one voting method to identifyerror-resilient collective outcomes and/or to weight votes to optimizethe collective probability of making a correct or optimal collectivechoice. This feature can be used if voters have rated all of the choicesin an agenda but only “voted for” the top choice in their preferenceordering. In this case, plurality votes lose information that could beused to identify error-resilient outcomes. Reprocessing the votinginputs in Borda, Condorcet, or Copeland scoring can make it possible toreveal error-resilient collective outcomes that would otherwise goundetected.

Another feature of the analysis of incoming votes under differentscoring methods is that weighting of votes to optimize the probabilityof rendering one or more correct or optimal collective choices enablesthe group to improve their performance. For instance, weightingindividual votes works efficiently to identify a single correct choiceunder plurality scoring, using a deterministic model ln (p/1−p) where pis the individual voter's probability of making a correct choice and 1−pis the individual voter's probability of making an incorrect choice.(Shapley, L. and B. Grofman, Optimizing Group Judgmental Accuracy inPresence of Interdependencies,” Public Choice, 1984). However, if thegoal is to select two or correct choices, plurality scoring will notperform as well as weighting individual approval votes by individualcompetence (Pinkham, R. and A. Urken, Competence and the Choice of aVoting System, unpublished manuscript, 1991).

This feature is also useful when the objective of a collective decisionis to assess risky choices under uncertainty. In this model, individualcompetencies would be estimated by a probability plus a stochastic errorterm. Then, as in the deterministic model, individual votes would beweighted according to the contribution of an individual voter to reducethe collective risk across all of the choices. This property ofstochastic dominance of collective performance is important because,unlike the deterministic model, it applies to a weaker, but generalconcept of dominance than is found in deterministic game theory, withits state-by-state dominance. The stochastic model can be defined to beindependent of complex and sometimes indeterminate tradeoffs associatedwith voter utility functions. This is particularly valuable forerror-resilience because the real-time computation of voting resultsmakes it infeasible to integrate utilitarian tradeoffs into rationaldecision making (Danthine, J. P. and J. B. Donaldson, IntermediateFinancial Theory, Prentice-Hall, 2002).

Another feature of the analysis of incoming votes under differentscoring methods is the use of deterministic or stochastic measures ofcompetence to weight individual votes to make it feasible to identifyerror-resilient collective outcomes that would otherwise not bedetected. This feature integrates competence and error-resilience(Urken, A., “Time, Error and Collective Decision System Support,”Proceedings of the International Conference on TelecommunicationsSystems, Oct. 5, 2003.)

Another feature of the analysis of incoming votes is the application ofmultidimensional gap analysis to identifying error-resilient collectiveoutcomes with and without the weighting of individual votes bystochastic and/or deterministic measures of competence. Collective gapsexist whenever voters rate and/or vote for choices scaled on more thanone dimension. For example, if voters rate a product's attribute onimportance and quality, the individual ratings for quality can beweighted by the rating for importance to create a weighted individualquality ratings. These weighted ratings can then be used to compute aweighted collective outcome. The normalized scores of the weighted andunweighted collective outcomes will then reveal shares of the totalcollective score for each choice. When the weighted and unweightedshares of the total score are the same, the gap equals zero and providesa measure of equilibrium along the dimensions of product importance andquality for the rated attributes. When the unweighted shares of thetotal score on particular attributes are larger than the weightedshares, the results indicate a positive gap: that these attributes areexceeding the collective expectations of product attribute qualityweighted by product attribute importance. However, when the weightedattribute shares of the total score are smaller than the unweightedattributes shares of the total score, the results indicate a negativegap: that these attributes are failing to meet the collectiveexpectations of product attribute quality weighted by product attributeimportance.

This feature is useful because in some cases, a collective choice maynot be error-resilient on one dimension, but be error-resilient onmultiple dimensions. For instance, a collective outcome based on dataabout product quality might not be error-resilient, but, when combinedwith product attribute importance ratings, the collective gap resultscould be error-resilient. Additionally, this feature is also useful whenindividual votes have been weighted using deterministic or stochasticmodels of individual and collective competence. Weighting individualvotes according to voter competence can reveal error-resilientcollective outcomes on a single issue. Extending the weighting of votesto more than one dimension can increase the scope of possibilities fordiscovering error resilient collective outcomes. Further, this featureis particularly useful when voters have varying competencies ondifferent dimensions. For instance, if a product were evaluated on thedimension of the number of attribute features and price competitiveness,voters who demonstrated a knowledge of the attributes of competingproducts and their pricing would have their votes weighted higher thanvoters who were ignorant of competing product feature lists and prices.Applying such weights and finding error-resilient collective outcomesproduces more precise and accurate guidance in interpreting collectiveoutcomes.

FIG. 4A is a diagram showing the user manager module 40 of FIG. 1 ingreater detail.

The user manager module 40 comprises a vote data collection module 42, avote database 43, a voter identification module 44, and a trust profilemodule 46. Data can be exchanged between each of these components usingthe common data exchange 60. The user manager module 40 utilizessettings from the agenda manager module 30 of FIG. 1 to administeragendas. A feature of user manager module 40 is to implementauthentication and encryption tools in the collection of data anddisplay of reports. This feature includes authentication options such asencrypted passwords, dynamic passwords (which are updated periodicallyfrom a remote server), biometric verification, challenge-responsetechniques for actively verifying attributes of a user, and otherpersonal and behavioral attributes of voter identification. The votedata collection module 42 encrypts and decrypts voting data transmittedacross a network. This feature increases the trustworthiness of thevoting process in wired and wireless networks by making it moredifficult for malicious intruders or processes (such as worms orviruses) to damage or replace submitted data.

Authentication settings can include plain text or encrypted usernamesand passwords, biometric input such as DNA, retinal scans, fingerprints, and/or voice prints as well as challenge-response techniquesthat verify users by Using background or location information. Optionsfor secure transmission include full encryption of messages as well asdistributed encryption techniques that allow partial disclosure of useridentities and partial sharing of information that is dependent on useidentity and authentication. Database security includes implementationof systems for protecting the server against intrusive viruses or usersas well as mechanisms for guarding against unauthorized access to thedatabase repository of voting information to users who might otherwisebe authorized users of the server or device on which the database islocated. Database security standards will vary depending on thearchitectural implementation of the invention as well as the processingspeed, energy capacity, and storage capability of the hardware devicesbeing used.

The voter identification module 44 implements demographic andattitudinal categories to monitor response patterns. These categoriesare created by the agenda manager module 30 of FIG. 1 and written in theagenda database 80 of FIG. 1. These patterns are used in generatinghistorical reports that facilitate the automatic scheduling ofrespondent targets for an agenda in the question creation module 32 ofFIG. 3A. The same patterns are used to limit participation by categoryand/or to prevent participants from submitting multiple responses in thevote data collection module 42.

The trust profile module 46 allows trust profiles to be created forusers. These profiles are useful in filtering votes in the computationof collective choice results because they provide flexibility inprocessing votes from respondents with different levels oftrustworthiness. This feature enables a user to see differences incollective choice results when the voting data are segmented into trustcategories. In some cases, there may be no difference between acceptingthe collective outcome produced by respondents in the most trusted andleast trusted categories taken separately and/or aggregated together.This knowledge enables a user to avoid taking blind risks ininterpreting collective outcomes. Further, this feature is important forerror-resilient and optimization decision tasks. In each type of task,trust profiles provide flexibility that is important not only to a user,but to others who receive notifications and/or reports from a user. Thisfeature can provide an additional level of information assurance thatadds credibility to notifications and reports.

A related feature of the trust profile module 46 is the collectiveassessments of trust. This feature allows a user in administrative roleto use the invention to create and distribute a new agenda using thequestion creation module 32 of FIG. 3A to reach out to trusted membersof a network community to obtain their perception of the trustworthinessof a voter. This information can become part of a trust profile or amodified trust profile stored in the agenda database 80 of FIG. 1 toexplore differential impacts on acceptance of collective outcomes forerror-resilient and optimization tasks.

Another feature of the analysis of incoming votes is the integration ofvoter trust profiles with scoring of collective choice results andidentification of error-resilient collective outcomes and production ofoptimized collective competence. The security of the user manager moduleof the present invention enables a host to assign trust profiles toincoming votes based on full identification of the voter or some degreeof quasi-privacy. Illustrative options for identifying voters anddefining trusted voter profiles in the present invention are used in thereport manager module of the present invention to generatetrust-sensitive collective choice results. For example, a user withadministrative privileges can set up a report to automatically sortvoters into different categories of trusted relationships, set limits onthe degree of acceptable trust for the task, and view an instantaneousanalysis of the collective results based on these settings. Thisanalysis also shows if the same or consistent collective choice resultsoccur when trust relationships are made more or less stringent. Thisinformation would allow a user to make interpretations of collectiveoutcomes that are informed by analysis of the implications ofdifferential trust relationships with voters. This feature adds anotherdimension of flexibility and sensitivity to the analysis oferror-resilience and maximizing the group probability of rendering oneor more correct or optimal collective choices. Trust profiles can reducethe risks associated with treating all voters as if they were equallytrustworthy. This feature can be used in a centralized, client-servernetwork architecture (FIG. 11) or a decentralized, peer-to-peer networkconfiguration (FIG. 12).

A related feature of this analysis is the ability to analyze votingprocesses in which the objective is to produce an error-resilient and/oroptimized collective outcome that contains a rank-ordering. For example,if the decision task is to achieve consensus on a ranking of the topthree choices in an agenda, this feature will allow a user to discoverthe conditions (including voting system—with multiple aggregation rules,trust profiles, group size, and demographic and attitudinal attributes)under which the decision objective can be satisfied. These features areimportant because they enable the user to gain broader knowledge aboutpossible collective outcomes. If the decision objective iserror-resilience, for instance, these features can increase thelikelihood of finding an error-resilient collective outcome. If thedecision objective is optimization, these features will expand the scopeof possible deterministic and stochastic outcomes that can optimizegroup performance. In addition, if error resilience and optimization arepart of a decision objective, seeing the results for other aggregationrules improves the likelihood that the user will find a solution.

The user manager module of the present invention determines the mediumor media that are used for data input and output in the presentinvention. This module includes options for using multimedia,mechanical, touch-screen, and optical devices such as mice, pens, andkeyboards, voice, and neurological data to enter data into the modulesand receive output. The user manager module uses different mediasuitable to the task at hand and provides redundant communication whennecessary. This functionality is useful because it enables a person touse voice commands to enter data, but choose among text, graphics, andmultimedia representations of data for receiving the output of themodules. This flexibility also allows blind or deaf users to chooseinterface media that they are comfortable using. Such user needs arealso supported by providing signing overlays for obtaining input anddisplaying output. For example, the needs of users from differentlinguistic backgrounds are supported by providing either visual or soundoverlays that can be set up in the user interface module (and therebymade accessible in the other modules and submodules of the currentinvention). The same flexibility allows a user to receive redundantoutput of results for important decisions. For instance, informationabout the analysis of a collective decision can be disseminated in text,numbers, and graphics in a module, but also sent by voice mail to assurethat the recipient receives a message as soon as possible.

FIG. 4B is a flowchart showing processing logic of the vote datacollection module 42 of FIG. 4A. Beginning in step 420, agenda securityitems are determined. Then, in step 422, voters are authenticated, usingany suitable authentication scheme such as passwords, biometricinformation, etc. A decision is made in step 424 as to whether allvoters have been authenticated. If a negative determination is made,step 422 is re-invoked, so that all voters can be authenticated. If apositive determination is made, step 425 is invoked, wherein adetermination is made as to whether to decrypt incoming votes. If anegative determination is made, step 427 is invoked, wherein the votesare stored in the vote database 43 of FIG. 4A. If a positivedetermination is made, step 426 is invoked, wherein the votes aredecrypted. Then, step 427 is invoked, wherein the votes are stored inthe vote database 43 of FIG. 4A. Processing of votes is then complete.

FIG. 4C is a flowchart showing processing logic of the voteridentification module 44 of FIG. 4. As mentioned earlier, the voteridentification module 44 implements demographic and attitudinalcategories to monitor response patterns of voters. In step 440, desireddemographics 441 can be selected for implementation. The demographics441 can include, but are not limited to: Internet Protocol (IP) addressreferral information, time of interaction, physical location, cookieinformation, user names, passwords, and biometric identificationinformation. Optionally, in step 442, a new demographic can be definedby a user and implemented. In step 443, the demographics are applied tothe voter population. In step 445, desired attitudes 444 can be selectedfor implementation. The attitudes 444 can include, but are not limitedto: preference patterns, comparisons to reference groups,collectively-determined attributes, self-rated competences, andperformance-based competencies. Optionally, in step 446, a new attitudecan be defined by a user and implemented. In step 447, the attitude isapplied to the voter population.

FIG. 4D is a diagram showing the trust profile module of FIG. 4A ingreater detail. The module 46 includes a number of trusted voter profiledetails, including aggregator ranking criteria 461 and collectiveassessment criteria 462. The aggregator ranking criteria 461 couldinclude voter attitudes, satisfaction of security criteria, demographiccriteria, frequency of participation, and special or historical responsetimes. The criteria information can be stored in a database, andaccessed by the modules of the present invention.

FIG. 5A is a diagram showing the report manager module 50 of FIG. 1 ingreater detail. The report manager module 50 includes a report creationmodule 52, a notification module 54, and a follow-up module 56.Communication and data interchange is provided between these modulesusing the common data exchange 60. The report manager module 50 providesa means for creating reports with different agenda data andcommunicating the results so that users can make use of the informationto take immediate action. The report creation module 52 implements oneor more report templates that provide standard options for analyzing andcommunicating the results of error-resilience and optimization analyses.This feature is important because it saves the user time and requiresless background knowledge for using the invention. These templates maybe selected and modified or used without modification. The user can alsobuild a new template from scratch or by modifying an existing templateand save it under a new name in the agenda database 80 of FIG. 1. Reporttemplates can be used with multiple agendas as long as the types ofquestions and their logical attributes (for example, the same number ofchoices and rating scale) are consistent.

The report creation module 52 also allows for the selection of a datasetin a report. This feature is set up with a default limiting a report tothe current agenda. However, this feature can be modified to includemultiple datasets selected by criteria such as beginning time, duration,respondent demographic and attitudinal attributes, and collective choiceresults. Examples of this last criterion are success rates in producingerror-resilient outcomes and achieving a minimum level of performance inmaking one or more correct or optimal collective choices. Once areferent set of agenda data has been defined, it can be used to define anew default for additional analyses. Additionally, the report creationmodule 52 automatically processes incoming vote data to enablecomputer-mediated groups to achieve the goal of producing anerror-resilient and/or optimized collective outcome.

A feature of module 54 is the creation and distribution ofnotifications. This feature is defined in FIG. 1 and monitored by usingaccess list controls. This feature targets notifications in terms of thesetups for communications media and type of information required. Forexample, notifications can include voice, text, and/or graphicalmessages distributed by wireless or wired communications devices.Notifications can be redundant in their use of message media and networkchannel. This feature is useful for updating final results of anerror-resilient and/or optimization analysis as well as updates onincreases or decreases in the likelihood of satisfying one or both ofthese decision making objectives. Further, this feature is also usefulbecause it allows the targeted respondents for a notification to selecta list of respondents based on the collective choice results. Forexample, respondents who individually and/or collectively indicated apreference or judgmental pattern in favor of an option could be selectedto update the access list to regulate communications with users. Thiscapability can also be used to send different notifications to differenttargeted respondents depending on individual and collective responsepatterns. In addition, notifications can be based on random or biasedsamples of respondents who collectively satisfy certain preference orjudgmental standards. This capability enables a user to qualify samplesto avoid or exploit hidden biases in the next round of respondent votingdata.

A feature of block 56 is the creation and use of followups. New queriesor actions can be targeted to respondents based on the creation andmodification of lists. New queries are new agendas that can be createdfrom scratch in or from templates. New queries can be automatedbeforehand so that the collective choice results trigger the selectionand administration of a new agenda. This feature is useful because itexpedites obtaining additional information that can change collectivechoice results. For instance, if an error-resilient indecisive outcomehas occurred, a followup agenda can be administered immediately toenable the group to reach a consensus. Similarly, if an optimizedcollective outcome does not meet a minimum standard of performance, afollowup agenda can be administered to another set of respondents to tryto obtain better results. Another feature of followups is theintegration of collective action or coordination of action that iscontingent on the collective outcome. This feature automaticallynotifies respondents about the outcome so that they can take immediateaction. In a client-server computer environment (FIG. 11), the centralnode has to distribute the results to the other nodes. However, in apeer-to-peer network environment (FIG. 12), the decision task can bedesigned so that each voter processes all of the other votes into acollective outcome and takes immediate action without waiting for acentral node to distribute news about the collective outcome. Anadvantage of peer-to-peer voting is that it eliminates the liability ofhaving a central node destroyed or disabled by a physical or cyberattack. This reliability is a distinct advantage as long as the decisiontask is designed to minimize the possibility that inconsistentcollective results are generated among the peers. The ability to designsimple and complex tasks to minimize this possibility is part of the newart enabled by this invention. This art is based on theoretical examplesand experimental data that identify the conditions under whichinconsistencies are likely to occur and empirical information thatcorroborates the effects of question and analytical techniques foravoiding or minimizing collective inconsistencies among peers.

The report manager module 50 allows user to plan and launch followupqueries to obtain more information and/or to use the notification toinvoke individual and/or collective contingency actions. First, thereport manager module can send or invoke a followup questionnaire bysending a target list of recipients to the agenda manager submodulealong with prescribed invocation of contingency questionnaire templatesor a new questionnaire created in the agenda manager submodule. Targetselection for respondents can be done on the basis of combinations ofdemographic and attitudinal and/or judgmental attributes. Queries can besent to the entire list or a random sample in which the respondentssatisfy minimum distributional requirements. An advantage of thissampling procedure is that the qualified sample is based on anunderstanding of multiple group attributes, not simply a small subset ofdemographic and/or attitudinal attributes. This technique isparticularly valuable when the decision task is multidimensional andrequires ratings of choices on more than dimension. This qualifiedsampling procedure allows an automated process or a browsing human ornode to understand the collective choice implications of a samplingprocedure and include conditions in the sampling process to avoid orcreate biased samples, depending on the followup decision taskobjective. This knowledge makes it possible to minimize or completelyavoid errors encountered in conventional offline or online polling(Brady, H. E. and G. R. Orren, “Polling Pitfalls: Sources of Error inPublic Opinion Surveys,” in Mann, T. E. and Owen, G. R., eds., MediaPolls in American Politics, The Brookings Institution, 1992).

A second option for gathering more information in followup queries is toask targeted voters or nodes to collect information from adjacent votersor nodes. This option has two advantages: communication reliability andredundancy. This outreach option provides reliability by making use ofthe network to collect voting information instead of expecting allrespondents to be able to make direct or indirect contact with the hostreceiving submitted votes. Using intermediary nodes to collect votes andretransmit them provides more complete information about the actualdistribution of preferences that can expedite the analysis oferror-resilient and/or optimal collective outcomes. Intermediary nodeswould use the modules and submodules shown in FIGS. 1 and 3A to collectand process votes from individual respondents. Depending on the numberof voters in a network, the complexity of the network hierarchy ofcommunications, network communications error conditions, and the timeconstraints for the decision task, sequences of intermediary datacollection and retransmission to a central node can be used. Redundantcollection of voting data may increase the load for choice processing,but this consequence can be offset by the opportunities for obtainingmultiple confirmations of submitted votes.

This intermediary data collection and retransmission feature is alsouseful because the redundancy provides opportunities to detect theexistence of network imposters and/or corrupted data. Detectinginconsistencies in redundant submissions enables a central processingnode to take an active role in managing risk in a dynamic wired orwireless network environment. Although wireless network environments aretypically considered to be more fragile than wired environments, bothtypes of networks could be equally vulnerable given a significantcombination of physical and cyber attacks on network nodes and links.Under emergency conditions, detected inconsistencies could trigger theuse of flexible voter trust profiles to process votes. Under theseconditions, contingency plans could also be triggered tocompartmentalize intruders and to implement countermeasures. Thisfeature can operate in centralized client-server mode (FIG. 11) or indecentralized peer-to-peer mode (FIG. 12).

The report manager module manages the application of scoring techniquesthat allow the group to reach an error-resilient and/or optimizedcollective outcome. Application design depends on the complexity ofdecision task, the number of users or participants, the hardware andnetworking environment, and the human requirements for operating theinvention to generate error resilient and/or optimized collectiveoutcomes. A representative embodiment of the present and uniqueinvention can use the collective logic of a scoring system to examineincoming sequences of votes to identify error-resilient collectiveoutcomes and/or to weight individual votes to optimize the collectiveoutcome. This application would be appropriate for small groups of usersusing wireless devices for periodic decisions. In contrast, when largenumbers of voters frequently submit data and access reports for complexdecision tasks, deterministic and/or stochastic statistical techniquesapplied to multiple scoring methods would be appropriate in therepresentative embodiment of the present and unique invention.

FIG. 5B is a diagram showing processing logic of the report creationmodule 52 of FIG. 5A in greater detail. In step 520, scoring inputs areretrieved from the agenda manager 40 of FIG. 1. Then, in step 521 adetermination is made as to whether to accept scoring defaults. If apositive determination is made, step 525 is invoked, wherein a votinggoal is determined. If a negative determination is made, step 522 isinvoked, wherein the scoring inputs can be revised using one or moretemplates. Then, step 525 is invoked. In step 525, a voting goal isdetermined from one or more voting goals 526. The goals could include,but are not limited to, error resilience, optimization, or a combinationof error resilience and optimization. Once the goal has is beendetermined, step 527 is invoked, wherein votes are processed inaccordance with the goal, using one or more of the error-resilient voteprocessing module 528, optimization vote processing module 529,plurality vote processing module 624, and approval vote processingmodule 640.

FIG. 5C is a flowchart showing processing logic of the error-resilientprocessing module 528 of FIG. 5B. Beginning in step 600, voters arescreened by analyzing the trust profile of each voter. In step 602, adetermination is made as to whether trust profile status information hasbeen obtained. If a negative determination is made, step 604 is invoked,wherein more information is obtained using the agenda manager module 30of FIG. 1. If a positive determination is made, step 606 is invoked,wherein a determination is made as to whether the obtained status isacceptable. If the voter trust profile does not meet the defaultcriteria set in one or more templates, the vote is not counted and isstored in the agenda database 80 of FIG. 1. If the trusted relationshipis ambiguous (because it does not meet all of the requirements foroutright rejection or acceptance), the vote can be stored in block 604(in agenda database 80 in FIG. 1) to obtain more information about thevoter. Obtaining more information can be done in an ad hoc or automatedway depending on whether the resolution of the status of voters withambiguous trust profiles is necessary for achieving the error-resilientand/or optimization goals of the group. If a voter's trusted status isacceptable, the vote is sent to block 610 to add to the current totalvote count or collective outcome.

The data from block 610 are periodically sent to block 612, whichoperates on the data to test it for error resilience. Such a test ispreferably is suitable to the energy and processing constraints of thedevice(s) on which the analysis is being conducted. For instance, on acurrent wireless phone or pocket computer, the test in block 612 couldcompare the collected and uncollected votes in a group of fixed size,examine the possible ways in which preferences could be distributed inthe outstanding voter population, and determine if the currentcollective outcome could be changed by any possible submission of votes.In step 614, a determination is made as to whether the collectiveoutcome is error-resilient. If a negative determination is made, step616 is invoked, wherein scoring settings are changed. Additionally, step618 is invoked, wherein an analyst is provide with the ability to savethe current votes and wait for additional votes to be added. Then, steps612 and 614 are re-invoked so that a new collective outcome can bedetermined and tested for error-resiliency. If a positive determinationis made in step 614, step 620 is invoked, wherein the system generatesnotifications, reports, and follow-up query agendas or contingencyactions using the report manager module. If a determination is made instep 614 that that it is unclear whether the collective outcome iserror-resilient, the risk analysis module 622 is invoked so that a riskanalysis can be performed.

FIG. 5D is a flowchart showing processing logic of the optimization voteprocessing module 529 of FIG. 5B. The processing achieved by this moduleis similar to the error-resilient processing module described earlier,except for the scoring of vote data. Beginning in step 530, voters arescreened by analyzing the trust profile of each voter. In step 532, adetermination is made as to whether trust profile status information hasbeen obtained. If a negative determination is made, step 534 is invoked,wherein more information is obtained using the agenda manager module 30of FIG. 1. If a positive determination is made, step 536 is invoked,wherein a determination is made as to whether the obtained status isacceptable. If the voter trust profile does not meet the defaultcriteria set in one or more templates, the vote is not counted and isstored in the agenda database 80 of FIG. 1. If the trusted relationshipis ambiguous (because it does not meet all of the requirements foroutright rejection or acceptance), the vote can be stored in block 537(in agenda database 80 in FIG. 1) to obtain more information about thevoter. Obtaining more information can be done in an ad hoc or automatedway depending on whether the resolution of the status of voters withambiguous trust profiles is necessary for achieving the error-resilientand/or optimization goals of the group. If a voter's trusted status isacceptable, the vote is sent to block 538 to add to the current totalvote count or collective outcome.

The data from block 538 are periodically sent to block 540, whichcomputes the collective probability of the voting group making one ormore collective choices. Such a test is preferably is suitable to theenergy and processing constraints of the device(s) on which the analysisis being conducted. In step 542, a determination is made as to whetherthe collective outcome is optimized. If a negative determination ismade, step 544 is invoked, wherein scoring settings are changed.Additionally, step 546 is invoked, wherein an analyst is provide withthe ability to save the current votes and wait for additional votes tobe added. Then, steps 540 and 542 are re-invoked so that a newcollective outcome can be determined and tested for optimization. If apositive determination is made in step 542, step 548 is invoked, whereinthe system generates notifications, reports, and follow-up query agendasor contingency actions using the report manager module. If adetermination is made in step 614 that that it is unclear whether thecollective outcome is optimized, the risk analysis module 550 is invokedso that a risk analysis can be performed.

FIG. 5E is a flowchart showing processing logic for performing botherror-resilient and optimization voting. Importantly, the presentinvention allows for the analysis of collective decisions of a votinggroup to determine whether the outcome is error-resilient, and whetherthe outcome is optimized (e.g., the collective probability of whetherthe group will make one or more collective choices). In step 554,individual deterministic and/or stochastic voting weights are computedusing the report maker. Then, in step 556, the weighted votes areprocessed using the error-resilient processing module 528 of FIG. 5B. Instep 558, scoring settings are changed, and in step 560, the reportmanager is invoked to provide notifications and follow-up tasks.

FIGS. 5F and 5G schematically illustrate the operation of voting orscoring method modules in the present invention. A feature of thepresent and unique invention is that is can incorporate any scoringalgorithm in the processing of voting data. Since every voting method iscomposed of a system of rules for voting endowment, vote allocation, andvote aggregation, the present invention can generate knowledge about theproperties of the application of a scoring algorithm that wouldotherwise be unknowable or a matter of conjecture. FIGS. 5F and 5G showhow the present invention processes votes under plurality voting andunder approval voting when the collective decision objective is toproduce an error-resilient and/or optimized collective outcome.

FIG. 5F is a flowchart showing processing logic of the plurality voteprocessing module 624 of FIG. 5B. In addition to determiningerror-resiliency and optimization, the present invention also allows forcollective outcomes to be determined using a plurality votingmethodology. Beginning in step 626, a determination is made as to thetype of decision task to be accomplished. If the decision is made toperform preference aggregation, step 628 is invoked, wherein only onevote is added to the collective outcome from each voter. This feature isuseful because it allows the counting process to take account ofdifferent types of vote submissions that are appropriate to the decisiontask. These types enable one vote per identified, partially identifiedand trusted voter, and/or anonymous voter as well a multiple votes froman identified, partially identified and trusted voter, and/or anonymousvoter.

If the determination is made to perform both preference aggregation andoptimization, steps 630 and 634 are invoked, wherein votes are weighedaccording to default or stored criteria and one weighted vote is addedto the collective outcome from each voter. The stored weights can beindividual or collective estimates and/or empirical performance measuresstored in database records. Further, the weights can be simple Condorcetdeterministic estimates of competence, Bayesian conditionalprobabilities, and/or stochastic measures of competence containing aprobability and error term. If a determination is made to performoptimization, step 632 is invoked, wherein votes are weighed accordingto a default criteria. In step 636, an aggregation rule is applied,which has been set by the agenda manager module 30 of FIG. 1. In step638, a determination is made as to whether the outcome iserror-resilient, using the error-resilient processing module 528 of FIG.5B. If a negative determination is made as to error-resiliency, theprocessing of module 624 is repeated. If a positive determination ismade, processing of module 624 terminates.

FIG. 5G is a flowchart showing processing logic of the approval voteprocessing module 640 of FIG. 5B. In addition to determiningerror-resiliency and optimization, the present invention also allows forcollective outcomes to be determined using an approval votingmethodology. Beginning in step 642, a determination is made as to thetype of decision task to be accomplished. If the decision is made toperform preference aggregation, step 644 is invoked, wherein only onevote is added to the collective outcome from each voter. This feature isuseful because it allows the counting process to take account ofdifferent types of vote submissions that are appropriate to the decisiontask. These types enable one vote per identified, partially identifiedand trusted voter, and/or anonymous voter as well a multiple votes froman identified, partially identified and trusted voter, and/or anonymousvoter. Further, this feature is important because it checks to make surethat the number of approval votes cast by each valid voter does notexceed the total number of choices. In addition, this feature isimportant because the error-resilient analysis takes account of thedifferent types of aggregation rules that are applicable in approvalscoring. For instance, plurality, majority, and unanimity aggregationrules can produce ties under approval scoring. Moreover, theseaggregation rules can be applied to the total number of approval votescast and/or the total number of voters casting an approval vote.Error-resilient analysis that takes account of these possibilities isuseful because it makes it possible for an analyst to differentiateamong different measures of consensus associated with these aggregationrules. Without the present and unique invention, these subtle, butpotentially dramatic differences would be not be detectable.

If the determination is made to perform both preference aggregation andoptimization, steps 646 and 650 are invoked, wherein approval votes areweighed according to default or stored criteria and one weighted vote isadded to the collective outcome from each voter. The stored weights canbe individual or collective estimates and/or empirical performancemeasures stored in database records. Further, the weights can be simpleCondorcet deterministic estimates of competence, Bayesian conditionalprobabilities, and/or stochastic measures of competence containing aprobability and error term. If a determination is made to performoptimization, step 648 is invoked, wherein approval votes are weighedaccording to a default criteria. These weighted approval votes are thenaggregated according to default rule(s) and analyzed to take account oftied plurality, majority, and unanimous outcomes as well as differentbaselines for computing these aggregation rules. In step 636, anaggregation rule is applied, which has been set by the agenda managermodule 30 of FIG. 1. In step 638, a determination is made as to whetherthe outcome is error-resilient, using the error-resilient processingmodule 528 of FIG. 5B. If a negative determination is made as toerror-resiliency, the processing of module 624 is repeated. If apositive determination is made, processing of module 624 terminates.

FIG. 5H is a flowchart showing processing logic of the risk analysismodule 622 of FIG. 5C. The risk analysis module 622 contains options forcomputing the likelihood of producing an error-resilient collectiveoutcome, determining the risk measures associated with the occurrence offactors in the collective choice process that can affect the productionof an error-resilient collective outcome, and sensitivity analysis oftrusted relationships among the votes of outstanding voters. Each one ofthese features includes options for generating reports, changing thescoring settings, and/or saving votes to await the collection of moreoutstanding votes. In step 658, a determination is made as to which typeof risk analysis is to be performed. Error-resilient outcome analysismodule 660 is invoked if the determination is made to test thelikelihood of producing an error-resilient outcome. Stochastic riskanalysis module 662 is invoked if the determination is made to perform astochastic analysis. Trust risk analysis module 664 is invoked if thedetermination is made to perform a trust risk analysis. After processingby modules 660-664 steps 666, 668, and 670 are invoked, wherein thevotes are saved and an analyst is granted the option of waiting foradditional votes, scoring settings are changed, and the report manageris invoked for notification and follow-up tasks.

FIG. 51 is a flowchart showing processing logic of the error-resilientoutcome analysis module 660 of FIG. 5H. The module 660 filters theincoming vote data for an agenda to differentiate agendas with fixed andunfixed voter populations. If the voter population is fixed, the votesare passed to block 674, where a scoring algorithm is implemented thatincorporates individual or collective estimates and/or empirical datafrom past collective decisions that enables the present invention tocompare the likelihood that incoming votes will conform to a patternthat will not change the current vote total or collective outcome. Thisfeature is useful because it allows an analyst to make a informeddecision about waiting for more incoming votes and/or taking alternativeaction. If the voting population is not fixed, similar likelihoods canbe computed by estimating ranges of increase in the receipt ofoutstanding votes. This feature is useful because it allows an analystto make an informed choice about the potential advantages anddisadvantages of processing more information about a voter populationthat is smaller (and/or larger) than the expected fixed number ofrespondents. After computing these likelihoods, steps 676 and 678 areinvoked, wherein scoring settings are updated and the report manager isinvoked for generating notifications and follow-up tasks.

FIG. 5J is a flowchart showing processing logic of the stochastic riskanalysis module 662 of FIG. 5H. The module 662 sorts the incoming datadepending on the number of voters associated with an agenda, andcomputes the probability of collecting enough votes to satisfy apre-defined aggregation rule. These computations use the currentcollected and uncollected votes and any known or estimated attributes ofthe voting population to compute the likelihood of receiving vote datathat enables an analyst to quantitatively estimate the risks associatedwith waiting for more data. This feature is useful because it providesguidance for the analyst in deciding how much more vote data must becollected in an uncertain environment to minimize risk to a targetlevel. After computing these likelihoods, steps 684 and 686 are invoked,wherein scoring settings are updated and the report manager is invokedfor generating notifications and follow-up tasks.

FIG. 5K is a flowchart showing processing logic of the trust riskanalysis module 664 of FIG. 5H. Unlike the sensitivity analysis usingthe trust profiles associated with collected votes using the dataattributes provided in FIG. 4D, the analysis in FIG. 5K computes the islikelihood of obtaining enough additional votes from the outstandingvoters that meet a prescribed level of trust. This computation is usefulbecause it enables the analyst to know how likely it is that additionalvoting information will be available for use in conducting anerror-resilient and/or optimization analysis. This knowledge can helpthe analyst in deciding if and when to seek additional information orimplement contingency actions. After computing these likelihoods, steps692 and 694 are invoked, wherein scoring settings are updated and thereport manager is invoked for generating notifications and follow-uptasks.

FIG. 6 is a diagram showing the sample data formats utilized by thepresent invention. Communication and data storage can be achieved by thepresent invention using any suitable data format. Examples of suchformats 70 include selectable text defaults 71, selectable graphicsdefaults 72, selectable video defaults 73, and selectable sound defaults74. Additionally, the user can add text options 75, graphic options 76,video options 77, and sound options 78, to expand the data formats 70 asdesired. These data formats allow for synchronous and asynchronouscommunication and interpretation of voting, textual, image, graphical,sound, animation, video (stored or live), quantitative, textual, andother information is organized to enable computer users to initiate andparticipate in collective decisions that identify error-resilientresults and/or use measures of competence to weight individual votes tooptimize the probability of producing a correct or optimal collectiveoutcome.

FIG. 7 is a diagram showing sample common data exchange communicationsformats utilized by the present invention. As mentioned earlier, thecommon data exchange 60 allows for communication between each of themodules of the present invention. Such communication could be performedover a wired channel 62, a wireless channel 64, or a hardware channel66. Any suitable communications medium can be used without departingfrom the spirit or scope of the present invention.

FIG. 8 is a diagram showing sample attachment formats 90 that can beused for exchanging information between voters using the presentinvention. The attachment formats 90 could comprise selectable textdefaults 92, selectable graphical defaults 94, selectable video defaults96, and selectable sound defaults 98. User-defined attachment formatscould also be utilized.

FIG. 9 is a diagram showing the architecture and sample record format ofthe agenda database 80 of FIG. 1. The architecture 102 could include acentralized architecture 104, wherein voting information is submitted toa central database file. Alternatively, the architecture 102 couldinclude a distributed architecture 106, wherein the database isdistributed over a plurality of computing systems. The agenda database80 includes a number of records 108 for storing information relating tovoting information. Those records could include, but are not limited to:agenda creator identifier; beginning and end dates; user identifiers;question types; titles; topics; rating scales; stored reports; databasehistory; memory storage options; and additional record types.

FIG. 10 is a diagram showing sample authentication, encryption, andtrust parameters utilized by the present invention. The authenticationparameters 112 could include user names, passwords, biometricidentifiers, challenge and response information, collective assessments,and new authenticators defined by users of the system. The encryptionparameters 114 could include wired and wireless encryption standards, aswell as new encryption standards defined by users of the system. Thetrust parameters 116 could include default specified by the user managermodule of the present invention, or new trust metrics defined by users.

FIG. 11 is a diagram showing a sample centralized voting architecture inwhich the present invention can be implemented. The system could beconfigured so that a mediator (John) receives votes from a plurality ofusers (Ed, Debby, Mary, and Steve). The centralized voting architecturecould be implemented in client-server computing architecture.

FIG. 12 is a diagram showing a sample decentralized voting architecturein which the present invention can be implemented. The system could beconfigured so that votes are submitted from each voter to every othervoter. The decentralized voting architecture could be implemented in apeer-to-peer computing architecture.

The error-resilient collective outcomes produced by the presentinvention were tested in numerous simulations and compared to benchmarkpredictions. In each simulation, the probabilities of reaching anerror-resilient collective outcome and the proportion of outstandingvoters were compared. The comparisons of both outcomes to benchmarkpredictions will be described now with relation to FIGS. 14A-23B.

FIGS. 14A-14C are graphs showing test results of error-resilientcollective outcomes produced by the present invention using random tiebreaking. In this simulation, three scoring methods including OnePerson-One Vote (OPOV) (also referred to as One Voter-One Vote (OVOV)),Approval Voting (AV) and Copeland voting were applied to each of threevoting populations including 10 voters, 100 voters, and 1,000 voters.Homogenous preferences were tested using a plurality aggregation rule,and random tie breaking was implemented. As can be seen in the graphs,homogenous preferences boost the error-resilient collective outcome(ERCO) efficiency under OPOV scoring as the number of voters increases,but the ERCO efficiency of AV and Copeland voting, which performs bestfor 10 votes, declines. This leads to the implications that: (1)controlling the expression of information via voting methods can improvethe probability of producing an ERCO regardless of the percentage ofoutstanding voters; (2) the takeoff of OPOV is consistent with theCondorcet theorem; and (3) crossover points provide options for choosinga voting method based on other considerations such as computationalenergy and overhead.

FIGS. 15A-15C are graphs showing test results of error-resilientcollective outcomes produced by the present invention using no tiebreaking. In this simulation, OPOV, AV, and Copeland scoring methodswere applied to each of three voting populations including 10 voters,100 voters, and 1,000 voters. Homogenous preferences were tested using aplurality aggregation rule, and no tie breaking was implemented. As canbe seen in the graphs, homogenous preferences boost ERCO efficiency, andCopeland voting performs marginally better than OPOV as the number ofvoters increases. This leads to the implications that: (1) preferencehomogeneity may compensate for loss of information derived from randomtie breaking; (2) ERCO efficiency of AV varies over a greater range thanwhen ties are randomly broken; and (3) Copeland voting tends to be moreERCO efficient than OPOV as the number of voters increases.

FIG. 16 is a graph showing variance and standard deviation outcomes forerror-resilient collective outcomes produced by the present invention,using 1,000 votes. As can be seen, ERCO variance is close to zero forsmall numbers of voters. For 1,000 voters, ERCO variance is significantfor AV regardless of the proportion of outstanding voters. ERCO varianceis normally close to zero, but increases sharply for OPOV and Copelandvoting when more than 90 percent of the voting information has not beenreceived.

FIGS. 17A-17C are graphs showing benchmark predictions for three scoringmethods using no tie breaking. In this simulation, OPOV, AV, andCopeland scoring methods were applied to each of three votingpopulations including 10 voters, 100 voters, and 1,000 voters.Homogenous preferences were tested using a plurality aggregation rule,and no tie breaking was implemented. As can be seen in the graphs, thebenchmark predictions can be higher for AV than those predicted by ERCOanalysis under the same conditions. This leads to the implications that:(1) benchmark predictions are as good as or better than ERCO for AV with10 and 100 voters; (2) unlike most ERCO metrics, benchmark predictionstend to get worse as the proportion of outstanding voters increases; and(3) benchmark results for all three conditions are close together.

FIGS. 18A-18B are graphs comparing the error-resilient collectiveoutcomes of the present invention and benchmark predictions forhomogenous preferences using a plurality aggregation rule and no tiebreaking. The ERCO results for 10, 100, and 1,000 voters are shown inFIG. 18A. The benchmark predictions are shown in FIG. 18B. As can beseen from the graphs, the benchmark predictions are higher than ERCOpredictions when the amount of outstanding voting information isminimal. Both types of predictions are very close when the number ofvoters is large.

FIGS. 19A-19B are graphs comparing the error-resilient collectiveoutcomes of the present invention and benchmark predictions forhomogenous preferences using a plurality aggregation rule and no tiebreaking. The ERCO results for 10, 100, and 1,000 voters are shown inFIG. 19A. The benchmark predictions are shown in FIG. 19B. As can beseen from the graphs, the benchmark predicts higher efficiency than ERCOpredictors under more than one condition. This leads to the implicationsthat: (1) for 10 voters, the benchmark metric predicts is higher ERCOefficiency than the ERCO analysis except when 90% or more of the votinginformation is outstanding; (2) Copeland voting with 1,000 votersconsistently produces higher ERCO efficiency than the benchmark metric;and (3) for 100 voters, there is not much difference between theCopeland voting and benchmark metrics.

FIG. 20 is a graph showing the effects of false positives (FP) on threevoting systems. The false positive was 0.01, which comprises a randomvariable subtracted from individual voter competence. The graph shows abimodal distribution of 100 voters, with 90 percent homogeneity withhigh competence (0.9), 5 percent heterogeneity with 50/50 competence,and tie breaking. As can be seen, a low false positive rate degradesperformance mostly under AV. OPOV is more sensitive to degradation thanCopeland voting when more than 90% of the outstanding voting informationhas not been received. Accordingly, choosing a voting system andmanaging the inherent risk depends on a number of tradeoffs. When 90percent of the outstanding voting information is outstanding, the votingsystems answer different questions. For example, Copeland voting showshow much more each choice is preferred to every other choice. OPOV showsonly the most frequently preferred top choice. Therefore, voting systemscan be question-specific depending on the question to be answered.

FIG. 21 is a graph showing the effects of false positives on threevoting systems. In this simulation, the false positive rate was 0.01,with a bimodal distribution, 100 voters, 90 percent homogeneity withhigh competence (0.9), 10 percent heterogeneity with 50/50 competence,and tie breaking. As can be seen from the graph, decreasing thepercentage of voters in the homogenous group reduces the ERCO efficiencyof Copeland voting to 0.95 and decreases the ERCO efficiency of OPOV to0.925.

FIG. 22 is a graph showing the effects of false positives on threevoting systems. In this simulation, the false positive rate was 0.1,with a bimodal distribution, 100 voters, 75 percent homogeneity withhigh competence (0.9), 25 percent heterogeneity with 50/50 competence,and tie breaking. As can be seen from the graph, decreasing the numberof voters in the homogeneous distribution degrades OPOV and CopelandERCO efficiency below 0.95 and drops Copeland from first place to secondplace with an approximately 10 percent decline in ERCO efficiency. Asthe false positive rate increases and the number of voters in thehomogenous distribution decreases, the difference in ERCO efficiencybetween OPOV and Copeland voting becomes noticeable when the proportionof outstanding voters is less than 90%. As the populations in the twodistributions become more equal, the threshold of differentiationbetween OPOV and Copeland voting will begin with a smaller percentage ofthe outstanding votes.

FIGS. 23A-23C are graphs comparing the probability of producingerror-resilient collective outcomes and benchmark predictions, usingOPOV, neutral competence, homogenous preferences, plurality aggregation,and no tie breaking. The benchmark predicts lower results than ERCOanalysis. The ERCO results are more volatile. When preferences areheterogeneous, ERCO analysis predicts lower but less volatile ERCOefficiency. This leads to the implications that: (1) homogeneityproduces volatile results when the number of voters is small andcompetence is not included in the model; (2) a greeter probability of atie and no tie breaking reduce ERCO efficiency under heterogeneity; and(3) for 5 voters, under some conditions, voting analysis predicts lowerERCO for AV and Copeland than the benchmark prediction.

FIGS. 24A-24C are graphs showing the efficiency and effects of time onthe probability of producing error-resilient outcomes. In thissimulation of 100 voters, 75 voters had homogeneous preferences and 0.48mean competence. 25 voters had heterogeneous preferences and 0.52 meancompetence. The false positive rate was 0.01 and the false negative ratewas 0.01. Shapley-Grofman weighting was applied, and ties were randomlybroken. As can be seen from the efficiency graph shown in FIG. 24A, evenwith moderate competence in a bimodal preference culture, Copeland ERCOefficiency reaches 0.9. When only half of the outstanding votes havebeen collected, Copeland ERCO efficiency is 0.85 or greater. Further, ascan be seen with reference to FIG. 24B, waiting 250 seconds produces aprobability of 0.85 of producing an ERCO when half of the outstandingvotes have been collected. Waiting for the remainder of the outstandingvotes only produces a 0.05 increase of producing an ERCO. The graphshown in FIG. 24C does not apply averaging. The time-to-ERCO efficiencypatterns are more complex when real variation is not simplified byaverages. ERCO efficiency increases monotonically, but is volatile.

FIGS. 25A-25C are graphs showing the efficiency and effects of time onthe probability of producing error-resilient outcomes. In thissimulation of 100 voters, 51 voters had homogeneous preferences and 0.48mean competence. 41 voters had heterogeneous preferences and 0.52 meancompetence. The false positive rate was 0.01 and the false negative ratewas 0.01. Shapley-Grofman weighting was applied, and ties were randomlybroken. As can be seen from the efficiency graph shown in FIG. 25A, in abimodal sensor culture with groups that are nearly equal in size thatshare moderate competence, ERCO efficiency barely exceeds 0.65 when halfof the votes have been collected. Additionally, there is a smallmarginal gain in ERCO efficiency derived from waiting for more than halfof the incoming voting information. Further, as can be seen withreference to FIG. 25B, waiting 250 seconds produces an approximately 30%improvement in ERCO efficiency. Waiting longer than 250 seconds producesa very small increase in ERCO efficiency. The graph shown in FIG. 25Cdoes not apply averaging, and shows a Rayleigh distribution with a meanof 5 seconds. The time-to-ERCO efficiency pattern is more complex whenthe actual variation of vote arrival time is not simplified by averages.ERCO efficiency increases monotonically, but is volatile.

FIGS. 26A-26B are graphs showing comparisons of the probabilities ofproducing error-resilient outcomes using three scoring methods (OPOV,AV, and Copeland) based on homogeneous and heterogeneous preferences.The parameters of this simulation were as follows: 100 voters, 75 voterswith homogeneous preferences and 0.9 mean competence, 25 voters withheterogeneous preferences and 0.48 mean competence, False PositiveRate=0.01, False Negative Rate=0.01, Shapley-Grofman weighting, and tiesrandomly broken. As can be seen in FIG. 26A, for homogeneouspreferences, the OPOV scoring method is optimal. However, as shown inFIG. 26B, for heterogeneous preferences, the Copeland scoring method isoptimal.

FIGS. 27A-27B are graphs showing comparisons of the probabilities ofproducing error-resilient outcomes using three scoring methods (OPOV,AV, and Copeland) and the effects of time on homogeneous andheterogenous preferences. The parameters of this simulation were asfollows: 100 voters, 75 voters with homogeneous preferences and 0.9 meancompetence, 5 voters with heterogeneous preferences and 0.48 meancompetence, False Positive Rate=0.01, False Negative Rate=0.01,Shapley-Grofman weighting, and ties randomly broken. As can be seen inFIG. 27A, for homogeneous preferences, the OPOV scoring method isoptimal. However, as shown in FIG. 27B, for heterogeneous preferences,Copeland voting is optimal.

For purposes of illustration of how the present invention can beemployed by a plurality of voters to produce expedited and accurategroup choices and resiliency to communications and decision-makingerrors, the following examples are provided.

EXAMPLE 1

In the first Example, a port security situation, such a physical andcyber attack on New York Harbor, has targeted the George WashingtonBridge (GWB), Lincoln Tunnel (LT), Holland Tunnel (HT), and VerrazanoBridge (VB). Emergency response teams (ERT's), coordinated by PortSecurity Headquarters in Hoboken (Portsec HQ), have been dispatched toeach targeted area in accord with state and national contingency plansdeveloped under the auspices of the Port Authority of New York and NewJersey (PANYNJ). Under this plan, each ERT contains mobile air and waterforces, health management teams, and three Nuclear, Biological, Chemical(NBC) units equipped to detect and manage terrorist attacks

The plan includes a contingency option that allows reallocation of twoNBC units from one or more of these locations to another. Thiscontingency allows flexibility in the management of crises to makeadjustments in the distribution of NBC units across the ERT's. An ERTcan initiate a request by from Portsec HQ in Hoboken. ERT requests canbe routed directly or indirectly to Portsec HQ.

Contingency planning has designated a team of five mobile observers toassess the current situation around the harbor and make a collectiverecommendation to the Commander at Portsec HQ. This team has access toGlobal Positioning System satellite feeds and other intelligence toenable them to monitor what is going on in each target area. Teammembers have been trained in the interpretation of different types ofintelligence and have participated in simulations in which they areforced to render decisions under stress. In these exercises, ERT's havelearned to implement adjustments derived from the recommendations fromthe mobile team. This special mobile team (SMT) of observers consists ofJohn, Mary, Ed, Dave, and Debby. Four of the observers each have primaryresponsibility for one of the attack targets; the fifth, Debby, is basedout of Portsec HQ.

The team at the Lincoln Tunnel is being overwhelmed by unexpectedoperational demands created by an NBC attack and requests more NBCsupport. Portsec HQ asks the special mobile team to provide a collectiverecommendation about NBC unit reallocation. The SMT know that theycannot depend voice communications to negotiate a consensus and thatwired and wireless communications infrastructure is unreliable. Howevertheir plans have anticipated this situation and created a CDSS thatallows them to reach a consensus despite undependable communications.Their task is to assess the number of NBC units required at eachresponse site so that resources can be reallocated to deal with theevolving crisis. Each team member takes account of the entire Portsituation and makes a recommendation about the number of NBC unitsneeded at each response site. Each person can recommend an increase ordecrease of by 2 units at each location or determine that no (zero)units should be allocated to a location. So the possible ratings are −2,0, or +2.

Table 1, below, shows the ratings of the SMT members for thereallocation options:

TABLE 1 NBC Unit Allocation Ratings from Five Mobile Observers John MaryEd Dave Debby Holland Tunnel −2 −2 0 −2 0 Lincoln Tunnel −2 0 2 0 0 GWBridge 0 2 −2 2 −2 Verazzano Bridge 0 0 0 0 2

These ratings are transformed into ordinal form in Table 2. This ordinalform shows which unit allocation options are rated higher than otheroptions. Cells that are in gray indicate a tie between pairs of choicesin descending order. Note that John's ordering includes two sets of tiedchoices, GWB and VB as well as HT and LT.

TABLE 2 Observer Ratings in Ordinal Form

In ordinal form, we can see what would happen if the ratings were scoredusing a voting mechanism based on a one-person, one vote system with aplurality aggregation rule. Under this is mechanism, one vote isassigned to first place ratings and the winner is the choice that hasthe most votes. As shown in plurality scoring column in Table 3, below,GWB, with three votes, is the plurality winner.

TABLE 3 Collective Outcomes Produced from Observer Inputs PluralityScoring Copeland Scoring Holland Tunnel 0 −8 Lincoln Tunnel 1 1 GWBridge 3 2 Verazzano Bridge 1 5

However, a weakness of this scoring procedure is intuitively obvious.GWB and VB are tied in John's preference ordering and, by chance, Johnentered GWB first and VBV second. If this tie were not resolved sohaphazardly, GWB would still be a plurality winner, but by a margin ofone vote, not two votes.

To gain a more granular interpretation of the rating data in Table 2,Copeland scoring can be applied. The Copeland method finds the Condorcetscore for each pair (i, j) of choices and then subtracts the Condorcetfor choice j from choice i to measure the net strength of therelationship between choices and across all pairs of choices. TheCondorcet score counts the number of times that each choice i is ratedhigher than each choice j. For instance, in Table 2, VB is preferred toGW twice, HT four times, and LT 2 times, for a total Condorcet score of7. However, if we subtract the countervailing Condorcet scores for GW,LT, and HT in pair-wise comparisons with VB, the results are 2, 1, and0, respectively. Subtracting these Condorcet scores, VB's net, orCopeland scores, become 0, 1, and 4, respectively, for a total Copelandscore of 5, as shown in Table 3.

In this scenario, extracting more information from the voting input dataenables us to avoid a voting mechanism error by identifying VB, not GW,as the location most critically in need of additional NBC units. Asimilar analysis can enable us to deal with the impact of communicationserror on collective decision-making outcomes.

The ability to reach a consensus about a collective outcome despitemissing voting data depends on the number of voters, the number ofchoices, the aggregation rule, the granularity of the preference datacollected, and the mechanism for scoring voting inputs. In this example,the goal is to find winner as quickly as possible so that the commandercan decide and implement necessary reallocations.

The example does not address the cases in which the error-resilientcollective outcome is a tied or an indecisive result. Knowing that aconsensus will be tied or indecisive without the missing data can bejust as important as identifying an error-resilient consensus. For thecommander can use this information to obtain more information to clarifythe situation. The CDSS could be designed to inform the commander if thetied or indecisive result represents a strong or weak consensus. Forinstance, if one of the tied choices is a Condorcet winner or if theindecisive outcome becomes decisive when re-scored under a differentvoting system, the commander could be presented with appropriate advice.If there is latent Condorcet winner, the CDSS might automaticallyresolve the presentation of the collective outcome to allow thecommander to take immediate action. Similarly, if an indecisive resultcan be resolved or if it cannot be resolved, the commander can bepresented with advice that allows implementation of immediate followupactions to obtain additional information and decide and implement areallocation decision.

However, if we assume that the goal is to identify a consensus, supposethat Postsec HQ has not received votes from Debby or Ed. In thissituation, it would be reasonable to pronounce GW the error-resilientwinner because Debby and Ed cannot possibly cast votes in any way thatwould change the plurality collective outcome.

In contrast, if Copeland scoring were used and Debby's votes weremissing, VB would be in second place (with a score of 2), while GWB(with a score of 3) would be in first place. However consideration ofthe ways in which Debby could rate the choices would reveal thepossibility of generating a Copeland score of 5 for VB, making it awinner. Similarly, if John's votes were missing, the Copeland outcomewould be a 3-3 tie between LT and VB, but logical analysis of possiblecollective outcomes generated by John's would disclose that the tie isnot a stable collective outcome.

The client-server model involves two risks: node failure and delays incollecting data and reporting the results. If a cyber or physical attackdisables or eliminates the Portsec HQ server, NBC units will not bereallocated and avoidable losses of life and property will occur. Evenif a single-point failure does not thwart emergency coordination, fataldelays may occur in collecting votes from the SMT, producing acollective outcome, and notifying the ERTs to take appropriate action.

To avoid these risks, the decision task can be redesigned to improve theefficiency of data collection and reporting and thereby increase theflexibility and efficiency of the ERTs. In the following peer-to-peermodel, the task is restructured to avoid burdening the central hostmanagers by presenting reallocation choices that incorporate informationabout which locations is should send an NBC unit to another location.Every ERT member sends their votes to every other member of the group;each member aggregates votes to produce a collective assessment ofspecific options for emergency reallocations of NBC units.

This formulation of choices also avoids a potential negative consequenceenabled by having the voters simply rate the relative needs of the fouremergency scene locations. If central host management is charged withreallocating NBC units, they may allocate resources in a way thatunwittingly exacerbates the plight of one or more locations. By askingfor a collective assessment of which locations should lose or gain anNBC unit, teams can internalize potential external negative consequencesin formulating their ratings and associated preference orders.

As shown in Table 4, this reformulated decision task asks the fiveemergency response team observers to approve of specific reallocationchoices. Notice that although a total of 12 approval votes are cast, the“winners” are those options that attract approval from a majority ofdecision makers (3 out of 5) even though no option gains a majority oftotal votes cast.

TABLE 4 Approved Allocations of Two Contingent NBC Units AllocationOptions John Mary Ed Dave Debby Totals HT to LT x 1 HT to GWB x x x 3 HTto VB x 1 GWB to HT 0 GWB to VB 0 GWB to LT x x x 3 VB to GWB x 1 VB toHT 0 VB to LT x 1 LT to HT 0 LT to GWB x 1 LT to VB x 1

The collective outcome in Table 4 is a tie, which in conventionalcollective decision-making, may seem like a problematic outcome. Howeverthe implication of the outcome makes sense in the context of the task.Under some circumstances, it may seem illogical not to shift an NBC unitfrom HT to the LT directly and then to move a unit from the LT to theGWB. But the judgment of the voters may be taking account ofintelligence and observations that indicate that shifting a unit fromthe LT to the GWB would be undesirable and/or infeasible.

In this decision task scenario, the marginal differences in the voteswould not seem to allow room to search for specific instances in whichmissing votes would not interfere with producing a collective outcome.However, since GWB to LT and HT to GWB account for 50% of the totalapproval votes cast, missing votes from one observer would not preventthe group from reaching a stable consensus.

This collective outcome is interesting because it demonstrates apotential CDSS problem. The result says that two NBC units should bemoved from GWB to LT and HT to GWB, respectively. But the logic of thedecision task suggests that the CDSS should check these results becauseit would be more efficient to move two NBC units from the HT to the LTand leave the GWB NBC units in place.

If we reinterpret the collective outcome in Table 4 using Condorcet andCopeland scoring, shown in FIGS. 3 and 4, the view of the results andpossibilities for tolerating missing votes increase. If the objective ofthe decision task is to select a single reallocation option, then HT toGWB, with a significant marginal lead over GWB to LT, can remain stableas the winner despite some missing votes. However, FIGS. 1 and 2indicate that the tied outcome under approval voting between GWB to LTand HT to GWB is weak masking not only a distinct expression of priorityfor HT to GWB, but also obscuring strength of support for VB to GWB. Soif the decision task is designed to allow ties, the more granularresults produced by Condorcet and Copeland scoring would not allow muchtolerance for accepting the tied collective outcome produced by approvalscoring.

This example illustrates the necessity of checking collective scoringresults under more than one voting system. For theoretically, approvalvoting is an efficient way of finding the Condorcet winner without doingall of the binary comparison arithmetic. This property can be importantto exploit in designing CDSS's that can operate within the energy andtime constraints of mobile devices. In small groups, the marginsproduced by collective outcomes can be so fragile that checking must bedone.

Another reason for double-checking collective choice arithmetic is todetect and resolve collective intransitivities and paradoxes produced bydifferent scoring systems (Arrow, K., Social Choice and IndividualValues, New Haven: Yale University Press, 1954; Condorcet, J. A. NMarquis de, Essai sur L'application de l'analyse à la Probabilité desDécisions Rendues à la Pluralité des Voix, Paris, 1785; Fishburn, P.“Monotonicity Paradoxes In The Theory Of Elections”, Discrete AppliedMathematics, vol 4, 119-134, 1982; and Gavish, B, and J. H. Gerdes, Jr,“Voting Mechanisms and their Implications in a GDSS Environment” Annalsof Operations Research, vol 71, 41-74, 1997). For instance, suppose thatapproval voting produced an outcome that selected GWB to LT and LT toGWB. This result would not make logistical sense. In addition, thisoutcome would illustrate the lack of a clear-cut or transitivecollective choice. With more voters, particularly under pluralityscoring, the probability of producing such outcomes increases. Theprobability of producing tied outcomes under approval voting can be fivetimes as likely as the distribution of voter preferences becomes moreheterogeneous. In a competence decision task, when voters haveheterogeneous preferences, the monotonically increasing function thatoccurs in the “jury theorem” as average voter competence increases canparadoxically become monotonically decreasing under approval voting(Pinkham, R. and A. Urken, “Competence and the Choice of a VotingSystem,” unpublished manuscript, 1991).

Reprocessing collective outcomes under alternative voting methods notonly detects such intransitivities, but it also enables the CDSS todetermine if the intransitive result persists under more granularscoring methods such as Copeland or Condorcet scoring. Detecting andresolving such problems could also be important for determining thetolerance of an outcome for missing data caused by communicationserrors.

These considerations are particularly important when voting isdecentralized. When each voter submits data to every other voter andreceives votes from every other voter, it is possible that collectiveoutcomes produced by different peers will be inconsistent. Suchinconsistency would lead the ERTs to work at cross-purposes andundermine the purpose of using voting to coordinate emergency actions.The decentralized strategy also has to contend with potentially seriouscommunications errors, but when the strategy is feasible, it may providestraightforward security and implementation advantages over acentralized strategy.

A representative embodiment of the present invention as a solution forthis resource allocation problem in a centralized computer environmentmakes use of the agenda manager module 30 in FIG. 1 to set up an NBCreallocation agenda and the question creation module 32 of FIG. 3A tocreate the questionnaire and set the scheduling including the timing andthe list of valid users. These settings are written in records for theagenda in the agenda database 80 of FIG. 1 and enforced by the usermanager module 40 of FIG. 1. Since downloading the questionnaires andrelated attributes is unrealistic in an emergency, a preexistingtemplate has been set up before the allocation of the ERTs so that thequestions can be accessed on the mobile iPAQ devices located with themobile ERT leaders at the emergency target sites. Access can be invokedby having a preset timer or by a message from an authorized member ofthe ERT network.

The five users enter responses and submit them in the agendaquestionnaire form presented in the question creation module 32 of FIG.3A after qualifying as respondents via the user manger module. As thevotes are collected and entered in the agenda database, a reporttemplate from report creation module 52 of FIG. 5A is applied to testfor error resilience. As soon as the analysis reveals an error-resilientoutcome, a notification message is sent to the commander at PortSec HQ,who can send a message to all of the ERTs about how to reallocate thescarce NBC resources.

These steps can be used when the invention is used in client-server(FIG. 11) or peer-to-peer mode (FIG. 12). However in the latter mode,the test for resilience would be designed so that the analysis can beimplemented despite the limited processing power and energy capacity ofthe mobile devices. The test design would be set up in the reportcreation module 52 of FIG. 5A. In addition, there would be no need touse the notification feature of notification module 54 of FIG. 5Abecause each iPAQ is running the present and unique invention, obviatingthe need to distribute information about the collective outcome fromPortSec HQ.

EXAMPLE 2

The second example illustrates the application of the present inventionto a competence decision task. Consider the following emergencyscenario. Five observers, John, Mary, Steve, Debby, and Ed are asked toobserve a convoy of vehicles passing from point A to point B. Eachobserver is to report the number of vehicles to a local commander, whowill then use the information to determine if resources are adequate toattack the convoy and dominate to achieve victory. If the convoy'sattributes make it risky to conduct an attack, the commander can avoiderror (and loss of life and resources) and regroup to plan anotherattack.

For simplicity, suppose the commander has informed the observers thatunconfirmed reports suggest that the convoy includes up to 7 vehicles.The commander knows—but does not tell his observers—that 5 or morevehicles—depending on their type—would make an attack unfeasible.

A conventional decision support approach might ask each observer toreport the number of vehicles and indicate the amount of confidence thathe has in this report. So for example, a form would allow Jim to report4 vehicles and indicate that lie feels confident about the report. Thisconventional approach is limited by two problems. First, it forces eachobserver to select a single number to report instead of allowingobservers to select a range of inputs. Indeed, some collective decisionsare designed to rule out the possibility that an observer can reportthat he saw 4 or 5 vehicles, so Jim would be forced to choose betweenthese two numbers. If Jim reports 4, he might be making an error by notindicating that there might be a number of vehicles that makes an attackinfeasible. But if Jim were able to report 4 or 5 vehicles, thisinformation could be integrated with reports from the other fourobservers to produce a more precise and accurate assessment.

Second, relying on self-rated confidence can be extremely undependable.Personality, decision task, and other factors often lead observers tooverrate their own competence in making choices. Moreover, even whenindividual self-ratings are relatively accurate, processing them withouta theoretical basis can produce disastrous results at the collectivedecision making level.

To remedy the first problem, our five observers are presented withchoices that do not force them to make risky, error-prone choices. Sincethe commander has set a limit of 5 vehicles as the threshold forattacking, the observers are asked to select a choice from 0, 1, 1 or 2,2, 2 or 3, 3 or 4, 4, 4 or 5, or 5 or more vehicles. This presentationof choices makes it less likely that an observer will undershoot orovershoot the correct number of vehicles.

To address the second problem, the confidence scale, representation ofconfidence ratings, and collective processing of input confidenceratings can be redesigned to minimize the error associated with usingself-ratings.

The redesigned confidence scale, shown below in Table 5, is used to askobservers to make a nominal classification of the confidence that theyassociate with a report. (Raters would only see the nominal categories.)These nominal ratings are converted into numerical probability estimatesthat their reports are correct. The scale is designed to allow observersto indicate that they are “not sure” about how likely or unlikely theirreports are to be correct.

The intent of the “not sure” category in this scale is to encourage anindividual to avoid two types of ratings errors that can makeself-ratings undependable:

TABLE 5 Self-Rating Response Scale for Convoy Assessment Task NotSomewhat Very Confident Confident Not Sure Confident Confident 0.2 0.40.5 0.6 0.8 Type 1 Error: Being confident about a wrong report, and Type2 Error: Being unconfident about a correct report

The motivation and capabilities of emergency responders can make themhighly motivated and adept self-raters of their competence. Emergencyresponders do not have to be perfect at rating their abilities for thevote mechanism model to work Normally, in academic and commercialexperiments, self-rating is eschewed because individuals tend tooverestimate their capabilities and the results can be volatile. But inthese tactical scenarios, all that is required is that raterself-confidence and actual ability be correlated closely enough toenable the model to approximate collective behavioral patterns.

When network communications conditions and time constraints permit,competence ratings can be based on a database of behavioral measuresderived from previous individual performance or collective assessmentsof individual performance. But in the current example, these assumptionsdo not apply.

Self-ratings can be aggregated in support of two goals. One goal is tocompute the likelihood that the collective assessment of the number ofvehicles is correct; the other goal is to weight the votes that are usedto generate the collective assessment of the correct number in order toincrease the group probability of making a correct decision.

To support the first goal, one can estimate the average individualcompetence of the voters and, if one assumes that group performance isequivalent to average individual performance, use the resultingprobability to determine if the reported number of vehicles issufficiently trustworthy to launch an attack. Alternatively, the average(self-rated) competence can be used along with other parameters of thevoting situation (the number of voters and the size of the majorityrequired to form a winning coalition) to compute the so-called Condorcet“jury theorem,” (Condorcet, 1785) an application of the binomialtheorem, to find the probability that the team of observers will make acorrect decision (Shapley and Grofman, 1984).

In the “jury theorem,” shown in FIG. 13, preferences are a randomvariable and a small change in the average voter competence can producea dramatic negative or positive effect on the probability that the groupwill produce a correct collective decision. The average voter competenceand jury theorem interpretations both offer precise, but not necessarilyaccurate, guidance in evaluating the collective report of the observers.For, as shown in FIG. 13, sometimes the group can do better (or worse)than an average voter. And for small groups (with fewer than 50 voters),the rapid rate of change that characterizes the jury theorem producesmore gradual increases in the group probability of making a correctcollective decision. Consequently, small groups require an averageindividual competence well over 0.5 to maximize the probability that thegroup produces a correct collective decision.

The Condorcet model computes the cumulative results that one wouldexpect to find in multiple experiments with the current parameters; themodel does not tell us how to intervene to maximize the production of acorrect choice in a particular collective decision.

Processing the self-rated competencies to weight the votes of the teamcan have a direct impact on the likelihood that a particular collectivedecision is correct. But if votes are weighted according to theproportion of times that an individual makes a correct choice, thecollective performance of the group will not be optimized. Instead,using ln (p/1−p) produces optimal results. A Monte Carlo experimentprovides empirical support for this rule (A. Urken, “Social Choice andDistributed Decision Making, in R. Allen, ed., IEEE/ACM Conference onOffice Information Systems, Palo Alto, 1988).

TABLE 6.1 Ratings of Choices for the Number of Convoy Vehicles ChoicesJohn Mary Steve Debby Ed 0 vehicles 1 vehicle 1 or 2 vehicles 2 vehicles2 or 3 vehicles x x 3 vehicles x 3 or 4 vehicles x x 5 vehicles morethan 5 vehicles

Table 6.1 above shows the ratings of the number of vehicles by ourfive-member team of observers. By inspection, it appears that thecollective outcome is likely to be indecisive if a majority is requiredfor a collective decision. If plurality were used without the tieredchoice alternatives, we could end up with a tie, a plurality for 2trucks, or a plurality for 3 trucks. However, if we take account of theself-ratings of the voters presented in Table 6.2, the nominalclassifications can be converted into individual probabilities suitablefor applying the Shapley-Grofman theoretical weights. Table 6.2 showsthe distribution of the self-rated competence data entered by our fiveobservers.

TABLE 6.2 Self-Rated Competence for the Number of Convoy VehiclesRatings Choices John Mary Steve Debby Ed 0 vehicles 1 vehicle 1 or 2vehicles 2 vehicles 2 or 3 vehicles 0.2 0.2 3 vehicles 0.5 3 or 4vehicles 0.8 0.8 5 vehicles more than 5 vehicles

Table 6.3, below, presents the derivation of the Shapley-Grofman weightsused to convert the self-rated competencies into weights that replacethe uniform single votes cast by the team of observers.

TABLE 6.3 Shapley-Grofman Weights p 1 − p In (p/1 − p) Weight AdjustedWeight 0.8 0.2 1.386294361 14 0.6 0.4 0.405465108 4 0.5 0.5 0 0 0.4 0.6−0.405465108 −4 0.2 0.8 −1.386294361 −14

Table 6.4 applies the Shapley-Grofman weights to observer votespresented in Table 6.1. Table 6.4 shows a wide margin of victory for the“3 or 4 truck” option: a 28-vote margin over the second-place choice anda 56-vote difference compared to the third-place outcome. By inspection,it is obvious that this collective outcome would be stable if John orMary's votes were missing. (For example, if Mary's vote were missing,the highest possible confidence/competence rating produced by Mary wouldonly produce a total score of 0 for the “2 or 3 truck” option.

TABLE 6.4 Individual Votes Weighted by In (p/1 − p) for Table 6.2Choices John Mary Steve Debby Ed Total Votes 0 vehicles 1 vehicle 1 or 2vehicles 2 vehicles 2 or 3 vehicles −14 −14 −28 3 vehicles 0 0 3 or 4vehicles 14 14 28 5 vehicles more than 5 vehicles

In the literature on voting, complex voting mechanisms are oftenportrayed as a source of uncertainty and chaos and as a means ofmanipulating collective outcomes. But, complex or heterogeneouspreferences can dampen collective incompetence, but constrain maximizingthe probability of making a correct or optimal collective decision.

Understanding the multifaceted impact of preferences and competence onthe production of collective outcomes can enable us to design votingmechanisms that are waitless and error-resilient. This section describesoptions for using complex choice patterns to achieve this objective.These options include multidimensional preferences, multidimensionalcompetence, and more flexible input mechanisms for expressingpreferences and judgments, and voter trust profiles.

Multidimensional preferences are based on the collective logic ofscoring preferences on more than one dimension, not classicmultidimensional scaling and descriptive statistics. For example, in theconvoy assessment problem, suppose that observers are asked to reportthe size and shape of the vehicles in addition to their number. They areasked if the size and shape of the vehicles are the same or different,but are also allowed to indicate that they are not sure about theseattributes.

In this scoring procedure, each observer's response on the number ofvehicles is weighted by their responses on the size and shapeattributes. For instance, in our convoy assessment example, Johnselected “2 or 3 vehicles.” If he selected “same” for size and shape,his input for vehicle number would be weighted by the rankings on thesize and shape scales to create a weighted individual rating for “2 or 3vehicles.” Then the weighted individual ratings for all five observerscan be aggregated to find the collective outcome produced by the defaultscoring mechanism. These weighted voting results can be normalized toshow the share of the total score gained by each choice in the decisiontask.

The differences between the shares of the total collective score underthe original vehicle number decision task results and the collectivescore weighted by size and shape reveal “collective gaps.” These gapscan be positive, negative, or zero. In repeated decisions, collectivegaps are normally distributed. But in a specific decision, gapsrepresent complex patterns that can be used to produce error-resilientand waitless collective outcomes.

To illustrate the possibilities for producing a consensus with gaps,suppose that our five observers rate all of the attributes following thesame pattern of individual agreement shown in Table 6.1. So John andMary, who selected “2 or 3 vehicles,” also agree that the size and shapeof these vehicles are the same. Mary, who spotted “3 vehicles,” reportsthat the size and the shape of the vehicles were different. And Debbyand Ed, who chose “3 vehicles,” disagree on the size and shapeattributes. Debby reports that the vehicles were the same size, but thatthe shapes were different.

In the original convoy assessment example, the ratings for the number ofvehicles in Table 6.1 would produce a tie between “2 or 3” and “3 or 4”vehicles under Condorcet scoring. Each choice receives 41.2% of thetotal collective score. When we broaden the task to encompass vehiclesize and shape with the observer inputs from the previous paragraph, thecollective gaps, shown in Table 7, are produced.

Gap Relationship Number and Size Number and Shape Gap Value for 2 or 3−55 −58 Vehicles Gap Value for 3 vehicles 16 18 Gap Value for 3 or 4 3939 vehicles

In this example, the gap analysis can provide a level of confirmationfor the collective assessment on a single dimension and be used to testfor error-resilience.

A representative embodiment of the present invention to implement thissolution would operate in client-server mode with templates 36 of FIG.3A preinstalled on mobile or wired devices. A message from the commanderor preset time would present the form for data input and the data wouldbe submitted over the network using the vote data collection module 42of FIG. 4A and written to the agenda database 80 of FIG. 1 on the convoyleader's wired or wireless computer device. A report would automaticallybe generated from the report template using the report creation module52 of FIG. 5A on the commander's user interface. If the commander usedthe multidimensional version of the decision task, a report templatewould be used to compute gaps. The results of these reports would bewould be sent redundantly using notifications by multiple communicationschannels so that the commander can use the collective assessment of theconvoy

EXAMPLE 3

In this example, the present invention is used to decipher intelligencefor investment decisions and to produce investment decisions themselves.In these tasks, error is not defined as a constraint, although it couldbe if wireless transactions were sufficiently trustworthy. However timeis still a significant constraint because speed in making decisions canprovide a competitive advantage in creating and exploitingopportunities. Moreover, since there is always the possibility of systemor human error, reaching a consensus as quickly as possible is essentialfor efficiency and effectiveness.

In practice, financial decisions are not made collectively in the sensethat assessments of intelligence and investment options are done bycollecting votes from individual investors to produce a collectiveoutcome. Relying on collective outcomes for intelligence assessments andinvestment decisions is considered to be too risky because the errorsare too costly. Allowing diversity and individuality in assessingintelligence and investment options provides a measure of stabilitybecause the group is better off than they would be if all decisions weredecided collectively.

However, in many organizational cultures, there is an informal operatingrule of unanimity that governs investment decisions. Colleagues operateindependently as long as their performance gains the tacit consent ofother members of their group. But if an individual's investmentperformance threatens the economic stability of the group, a coalitionof colleagues may eliminate the deviant performer. This modus operandiis essentially a reactive way of dealing with the problem of managingrisk. The present and unique invention can be used to control thecollective decision making process and gain the benefits and avoid thelosses from relying on collective outcomes. This control would allowinvestment practices to be governed in a proactive instead of a reactiveway. For example, in bond trading, traders make daily individualpredictions about indicators such as unemployment and federal interestrates in managing their portfolios. If these factors were assessedcollectively using the present and unique invention, accuratepredictions could be produced without waiting for all of the votes to bereceived from other traders. This intelligence would be updated andcalibrated daily to advise each individual if and when his/herindividual performance in predicting key indicators is better than thegroup's predictive performance. This waitless collective intelligencecan provide a significant tactical advantage in creating and exploitingmarket opportunities. In short, it can be an error to wait when there isan alternative that makes waiting unnecessary.

Since relying on collective decisions about investments is regarded asif it were putting all one's eggs in one basket, it is not surprisingthat investment organizations have not acted collectively. But arepresentative embodiment of the present and unique invention providesthe benefits of enhanced, optimized collective decisions without therisks of catastrophic collective losses. By updating and calibrating theratio of successes to failures in individual investment decisions, thecollective decision system support mechanism can allow individuals toknow if the conditions are sufficient for the collective decision tooutperform the most competent investor(s) and make it rational tovoluntarily accept collective investment recommendations. If thecollective decision system support is designed to operate on short termdecisions, acceptable rates of error can be predetermined to assure thatthe marginal impact of the system on profits is beneficial rather thanharmful.

A representative embodiment of the present invention to solve decisionproblems in this scenario can be implemented in a centralized (FIG. 11)or decentralized (FIG. 12) computer networking environment by setting upagendas using the question creation module 32 of FIG. 3A. These agendascan be templates that are automatically presented each trading day at acertain time for user input. Respondents could also be notified by mailor other communications channel to answer the questions about keyindicators and options for allocating bond portfolio investments.Responses would be submitted to using the vote data collection module 42of FIG. 4A and written into agenda database 80 of FIG. 1. A reporttemplate created using the report creation module 52 of FIG. 5A wouldthen automatically extract the incoming data to test for outcomes thatsatisfy error resilience and optimization requirements. Results would besent as a notification using the notification module 54 of FIG. 5A tousers who satisfied the security settings in the vote data collectionmodule 42 of FIG. 4A.

EXAMPLE 4

In this example, the present invention is applied to provide stable andefficient dynamic routing of electricity in networks. This applicationis similar to the distributed routing of phone calls in networks (Urken,A., “Coordinating Agent Action via Voting,” Proceedings of the IEEE/ACMConference on Office Information Systems, 1990). The key differences arethe measurement of voter preferences and competence and the addition oferror-resilience. Routing decisions are made automatically by acollective decision of nodes rather than a single node or a humancontrolling a single node. Routing preferences are inversely related tothe latency or backup in transmitting electricity. As energy moves moreslowly through certain routes, local nodes immediately adjust theirpreferences to route electricity via routes that are less congested.This dynamic routing minimizes the likelihood of having large portionsof the network overloaded so that nodes and transmission routes fail orare taken out of service automatically to minimize damage. The aggregateeffect of this cascading failure is a blackout that can causesignificant social and economic damage to a modern economy.

A representative embodiment of the present invention to solve thedecision task in dynamic routing of electricity in networks would beconfigured in local peer-to-peer networks (FIG. 12) within the nationalgrid for managing electricity. The design of the network configurationwould depend an assessment of the tradeoffs between creation of smallgroups of nodes and their capability in managing and redirectingelectricity quickly enough to make a sufficiently significant marginalimpact in dynamic situations. The skill in designing this configurationis part of the existing art in managing electricity networks, butrequires the integration of a new technology-driven art enabled by thepresent invention.

Each node in the network would have a copy of the present invention witha predefined set of network nodes as fellow voters in a local areanetwork. All of the nodes would be programmed to answer a single agendaevery fraction of a second. The periodicity of these responses would bedetermined from the expertise of electrical engineers and tested withMonte Carlo simulations to determine an acceptable degree of errorresilience. The agenda, created in the question creation module 32 ofFIG. 3A, would collect votes for a fixed list of routing alternatives inthe vote data collection module 42 of FIG. 4A. To expedite processing,each peer node would collect the data in real time memory and carry outan error-resilient analysis and then write the data and the result tothe agenda database 80 of FIG. 1.

A representative embodiment of he present and unique invention enhancesthe power of dynamic routing by enabling collective decisions to beimplemented more quickly so that adjustments in network allocations ofelectrical flows can be expedited to minimize and/or preclude thedestabilizing effects of cascading network communications failures thatculminate in blackouts. Moreover, if these decisions are done in apeer-to-peer architecture, the decisions can be structured so that allvoters carry out the collective decision without having to wait for theconsensus to be computed and announced by a central node.

EXAMPLE 5

In this example, the present invention can be applied to enablecollections of mobile sensors to provide more precise and accurateintelligence. Currently, when sensors report readings for phenomena,their representations are presented in terms of a mean and a measure ofvariance. This statistical summary is efficient because it prevents thesensor from having to store and submit larger amount of data that wouldexceed its processing and energy capacity. A drawback of these summaryreports is that it does not convey information about the distribution ofthe variance that represents the relative frequency of occurrence ofdifferent values of the reported phenomenon. Although this type ofinformation is summarized in a Pearson distribution, the complexity ofthe calculation and the data transmission requirements make itinfeasible to use this type of representation in reporting to a centralnode.

However the representation and submission of data can be made moreaccurate and efficient if node reporting is treated as if it involved acollective decision process. In this process, the sensors rank thevalues of the rating scale for the phenomenon on an ordinal or cardinalscale and submit their ratings or votes to a central node forprocessing. Depending on the task, the ordinal data can be processedwith Borda, Condorcet, Copeland, and/or other scoring algorithms toanswer different questions about the aggregations of the individualsensor reports. Cardinal data can be processed using point voting.

The present invention can enhance this aggregation process by providingerror-resilient analysis to identify a consensus as quickly as possible.Since sensors are a part of an emergency detection and warning system,waiting to collect information is erroneous if it is not necessary.Expedited determination of a consensus allows the users of intelligenceto take immediate action to evade a phenomenon, to implementcountermeasures, and/or, if necessary, to obtain additional informationto clarify uncertainties and trends.

A representative embodiment of the present invention can be implementedin centralized network mode (FIG. 11) or decentralized network mode(FIG. 12), though normally the former mode is used in connection withdata collection from sensors. In the centralized implementation, eachsensor would repeatedly enter data into a preexisting form based on itsreadings of the phenomenon it is monitoring. In this example, thedecision task created in the question creation module 32 of FIG. 3Awould be a rank ordering of detected levels of a phenomenon. Nodes wouldsubmit their ratings or rankings using the vote data collection module42 of FIG. 4A. In this case, the report creation module 52 of FIG. 5Acan make use of security protections in the vote data collection module42 of FIG. 4A and the trust profile module 46 of FIG. 4A to add a levelof trustworthiness to the analysis of the results. Reports can be sentusing the notification module 54 of FIG. 5A. In case of preset reportsettings being triggered by incoming data that spawn followups in thefollow-up module 56 of FIG. 5A, contingent actions in this module can beinvoked. Alternatively, followup queries using preset agendas fromtemplates 36 of FIG. 3A can be sent to node and human respondents toobtain more information about the phenomenon and options for takingaction to compartmentalize the phenomenon or to counteract its affects.

The present invention could be applied to manage traffic in variouslocations. Management of traffic in automobile and plane travel is aserious problem because control systems do not exist that allow driversand pilots to make dynamic adjustments to coordinate their decisionsabout scheduling and routing their trips. In automobile traffic, relyingon real-time helicopter reports can be unreliable either becauseassessment of existing and alternative route conditions is wrong orbecause broadcasting traffic advice produces mass shifts in trafficpatterns that can overload alternative routes. To remedy this problem,assessments of traffic conditions and alternative routes can be mademore precise and accurate by drawing on the collective intelligence ofdrivers, whose ability to assess traffic conditions and recommendalternative routes is based on experience and learning derived fromcommuting. Drivers could either initiate or respond to ad hoc polls thatwould be processed using error-resilient systems and methods to deliverprivate reports to poll participants to guide their choices. These pollscould provide types of information that cannot be derived fromconventional traffic information systems. For instance, a poll couldprovide advice about when to change a route on a congested road. Knowingwhether to take the next exit or risk waiting to see if traffic improvesand if does not to take a later exit can save drivers time and money.For drivers of emergency vehicles (in civilian or military situations),error-resilient feedback can save many lives and avoid damage toproperty This solution could use self-confidence and/orperformance-based ratings to dynamically adjust the competence orreliability weights of the voters.

In the case of air traffic, the lack of dynamic intelligence createsgridlock and monetary losses from cancelled flights and delayed arrivals(H. W. Jenkins, Wall Street Journal, Aug. 18, 2004, The ComingRevolution In Air Traffic Control, Page A11). Proposals to use globalpositioning systems (GPS) to automatically control flight paths do notinclude mechanisms that are error-resilient. Collective intelligencebased on the dynamic decisions of software that supports human ormachine intelligence can remedy this limitation. When malicious orinadvertent errors would lead to crashes, the present invention, bytreating the problem as a collective decision or voting problem, wouldallow the production of instantaneous and accurate decisions tocoordinate the choices flight paths to assure safe management of airtraffic. When air traffic controllers are involved, the use of waitless,error-resilient collective intelligence can provide a means of managingerror in complex systems for controlling air traffic (see, e.g., DavidLeadbetter, Andrew Hussey, Peter Lindsay, Andrew Neal, and MikeHumphreys, Towards Model Based Prediction of Human Error Rates inInteractive Systems, 0-7695-0969-X/01, 2001 IEEE.)

The present invention could also be implemented to process inputs fromvarious sensors. The cost, speed, and reliability of sensors is a basicconstraint on the use of sensor arrays in emergencies. (see, e.g.,Behrooz Parhami, Multisensor Data Fusion and Reliable MultichannelCommunication, IEEE: ASILOMAR, 1996) Sensors that are reliable are alsovery expensive. But even costly sensors may fail, take long to processsensed data, and have their messages delayed by network communicationscongestion or blocked by breakdowns in communications links. Currenttechniques of sensor “fusion” do not take account of problems incollecting and processing information in complex computing environmentsin which a) sensors are perfect, but network communication is imperfect,b) sensors are imperfect, but network communications is perfect, or c)sensors and network communications are both imperfect.

In these risky environments, the use of collective decision systemsupports (CDSS) methods and systems can extend the scope of sensoroperations. (see, e.g., Trent W. Lewis and David M. W. Powers, SensorFusion Weighting Measures in Audio-Visual Speech Recognition,Australasian Computer Science Conference, 2004.) For instance, ad hocnetworks of sensors could be created by dropping numbers of sensors intoan area. Low cost sensors with moderate or even “low” reliability can beused because CDSS methods and systems can produce high collectivereliability by making use of the present and unique invention.Conventional fusion techniques that rely simply on a majorityaggregation rule do not provide the reliability or precision andaccuracy that can be provided by the present invention.

Designers of new multi-functional sensors that sample and detectmultiple agents have proposed installing thousands of sensors in UScities (see, e.g., Philip J. Wyatt, Early Warning and Remediation:Minimizing the Threat of Bioterrorism, Journal of Homeland Security,April, 2002). But such proposals do not take account of transmittingsensed data over a centralized or distributed network in whichinadvertent or malicious errors can thwart the delivery of intelligence.The present and unique invention provides a solution to this problemthat takes account of differences in sensor reliability and speed andprovides specific advice about how much information must be received orhow long a recipient must wait to reach a decision and take action.

FIGS. 28A-28B are graphs showing comparisons of the probabilities ofproducing error-resilient outcomes for three scoring methods (OPOV, AV,and Copeland) in various sensor networks. In this simulation, 100sensors were simulated, with a 75-25 sensor split (FIG. 28A) and a 95-5sensor split (FIG. 28B). As can be seen, the ERCO result does not changein response to changes in sensor splits. Importantly, the ERCO resultsproduced by the present invention can be used to determine an optimalnumber of sensors to activate in a network, thereby conserving resourcesand/or energy.

FIGS. 29A-29B are graphs showing comparisons of three scoring methodsand effects of time on the production of error-resilient outcomes invarious sensor networks. The parameters of this simulation are the sameas those for the simulation shown in FIGS. 28A-28B, with 75-25 and 95-5sensor splits, respectively. As shown in FIG. 29A, a 75-25 split resultsin some volatility in the OPOV scoring method over time, wherein theother scoring methods (AV, Copeland) diverge over time. As shown in FIG.29B, a 95-5 split results in less volatility in the OPOV scoring methodover time, wherein the other scoring methods (AV, Copeland) do notdiverge over time.

PSEUDOCODE EXAMPLE

The logic of the present invention can be illustrated by the followingpseudocode example:

1. Use the user interface engine module submodule in FIG. 1 to set upthe questionnaire and timing for collection of data in the agendasubmodule and define security rules and eligible voters in the usermanager submodule, and define notifications and report privileges in thereport maker submodule in FIG. 1.

2. Use the agenda manager submodule of FIG. 1 to define a votingobjective and a system for carrying out error-resilient and/oroptimization analysis:

If the objective is to reach a consensus that satisfied plurality,majority or another aggregation rule under one or more voting methods,take account of the number of voters, the complexity of the decisiontask(s), the configuration of the network vote collection process, andthe processing capabilities and energy capacity of the network devicesused in the voting process to choose a method of adaptive scoring.

If the number of voters is small and the decision task is not verycomplex, use the logic of the collective decision making process toassess error resilience as votes are received.

If the number of voters is large and the decision task is complex, usean adaptive scoring mechanism to ascertain error-resilience that isappropriate to the processing and energy attributes of the hardwaredevices in the network. If the task includes options for gap analysis,configure the scoring mechanism to take account of computationaldemands.

If the decision objective is to optimize the probability that a groupwill make one or more correct or optimal choices, choose a scoringmechanism that takes account of the number of voters, the complexity ofthe decision task, and the network architecture.

Apply stochastic and/or deterministic measures of competence dependingon the decision objective.

Apply approximations when processing capacity is constrained by timeand/or energy constraints.

3. Use automated or semi-automated analysis of the results innotifications and determination of collective resilient and optimizedcollective outcomes.

4. Link automated templates and procedures for targeted followup queriesand actions.

5. Enable human decision makers to use the reports in an advisory modeto make the final decision about followup queries and actions.

Having thus described the invention in detail, it is to be understoodthat the foregoing description is not intended to limit the spirit andscope thereof. What is desired to be protected by Letters Patent is setforth in the appended claims.

1. A system for producing error-resilient collective group decisionscomprising: a plurality of computing systems interconnected by acommunications network, each of the plurality of computing systemsincluding a user interface for allowing communication with a voter ateach computing system; an agenda manager module for creating andpresenting at least one question to be voted on to each voter using theuser interface; means for calculating a voting termination point and avoting time period sufficient to achieve a desired voting objective inthe presence of incomplete votes by modeling said desired votingobjective using a plurality of vote scoring methods; a user managermodule for controlling interactions between each voter and receivingvotes up to the voting termination point; and a report manager modulefor processing the votes by applying a plurality of vote scoring methodsto produce a collective group decision that is resilient to errors. 2.The system of claim 1, wherein the voting termination point iscalculated based upon a predetermined number of votes.
 3. The system ofclaim 1, wherein the voting termination point is calculated based upon apreset time period for voting.
 4. The system of claim 1, wherein theagenda manager module calculates a number of voters required to reachthe voting objective.
 5. The system of claim 1, further comprising acommon data exchange for allowing communication between the userinterface, the agenda manager module, the user manager module, and thereport manager module.
 6. The system of claim 5, further comprising atleast one common data format for exchanging information over the commondata exchange connected to a database.
 7. The system of claim 1, furthercomprising an administrator for supervising a voting process; and ananalyst for analyzing collective outcomes.
 8. The system of claim 1,wherein the agenda manager module comprises a question creation modulefor creating questions to be voted on.
 9. The system of claim 8, whereinthe agenda manager module comprises a plurality of voting templates tobe applied to the questions, the voting templates implementing one ormore voting rules, which rules implement one or more scoringmethodologies for achieving an optimized voting group outcome and definevote endowment, voter competence, vote allocation rules for each voter,and an aggregation rule for collecting votes.
 10. The system of claim 1,wherein the user manager module comprises a vote data collection modulefor collecting the votes and for controlling encryption and decryptionof votes submitted by users; and a voter identification module, whichidentifies permitted voters and analyzes voter demographics andattitudes.
 11. The system of claim 10, wherein the user manager modulefurther comprises a trust profile module for determining voter trustlevels.
 12. The system of claim 1, wherein the report manager modulecomprises a report creation module for creating voting reports and foridentifying pre-defined voting objectives.
 13. The system of claim 12,wherein the report creation module processes the votes usingdeterministic or stochastic vote weights and using voter trustinformation, the report creation module including an error-resilientvote processing module, which computes the probability of collectingenough votes to produce an error-resilient outcome.
 14. The system ofclaim 12, wherein the report creation module comprises an approval voteprocessing module, which computes the probability of collecting enoughvotes to satisfy an aggregation rule.
 15. The system of claim 12,wherein the report creation module comprises a trust risk analysismodule, which computes the probability of collecting enough trustedvotes to satisfy an aggregation rule.
 16. The system of claim 12,wherein the report creation module comprises a plurality of voteprocessing modules.
 17. The system of claim 12, wherein the reportcreation module comprises an optimization vote processing module. 18.The system of claim 12, wherein the report manager module comprises anotification module for notifying voters of voting results.
 19. Thesystem of claim 18, wherein the notification module provides anindication of whether the collective group decision is resilient tocommunications or decision-making errors.
 20. The system of claim 19,wherein the notification module suggests gathering additional votes ifthe collective group decision is not resilient to communications ordecision-making errors.
 21. The system of claim 12, wherein the reportmanager module comprises a follow-up module for submitting follow-upquestions to voters and instituting contingency plans after a collectivegroup decision has been made.
 22. The system of claim 1, wherein thecommunications network comprises a peer-to-peer network.
 23. A methodfor producing an error-resilient collective group decision from aplurality of voters on a communications network comprising: establishinga voting agenda having at least one of question to be voted on;determining a voting objective; presenting the voting agenda to each ofthe plurality of voters; calculating a voting termination point and avoting time period sufficient to achieve a desired voting objective inthe presence of incomplete votes by modeling said desired votingobjective using a plurality of vote scoring methods; allowing each ofthe plurality of users to vote; receiving votes until the votingtermination point; and processing the votes with a plurality of votescoring methods to produce a collective group decision that is resilientto errors.
 24. A method for deploying resources comprising: providing acommunications network interconnecting a plurality of voters with acommand center; issuing a voting agenda from the command center to eachof the plurality of voters; calculating a voting termination point and avoting time period sufficient to achieve a desired voting objective inthe presence of incomplete votes by modeling said desired votingobjective using a plurality of vote scoring methods; allowing the votersto vote; terminating voting at the voting termination point; processingthe votes using a plurality of vote scoring methods to produce acollective group decision; and deploying resources based upon thecollective group decision.
 25. A method of acquiring data from aplurality of sensors comprising: providing a communications networkinterconnecting a plurality of sensors with a central processor;determining a plurality of vote scoring methods; calculating a dataacquisition termination point and a data acquisition time periodsufficient to achieve a desired voting objective in the presence ofincomplete data by modeling said desired voting objective using theplurality of vote scoring methods; acquiring data from the plurality ofsensors until the data acquisition termination point; and processingacquired data using the plurality of scoring methods to produce acollective group decision.